Regulating Robo Advice Across the Financial Services Industry

University of Pennsylvania Carey Law School University of Pennsylvania Carey Law School

Penn Carey Law: Legal Scholarship Repository Penn Carey Law: Legal Scholarship Repository

All Faculty Scholarship Faculty Works

2018

Regulating Robo Advice Across the Financial Services Industry Regulating Robo Advice Across the Financial Services Industry

Tom Baker

University of Pennsylvania Carey Law School

Benedict G. C. Dellaert

Erasmus University of Rotterdam

Author ORCID Identi;er:

Tom Baker 0000-0002-0876-2312

Follow this and additional works at: https://scholarship.law.upenn.edu/faculty_scholarship

Part of the Administrative Law Commons, Banking and Finance Law Commons, Business Law, Public

Responsibility, and Ethics Commons, Computer Law Commons, Insurance Law Commons, Law and

Economics Commons, Robotics Commons, Science and Technology Law Commons, Securities Law

Commons, and the Technology and Innovation Commons

Repository Citation Repository Citation

Baker, Tom and Dellaert, Benedict G. C., "Regulating Robo Advice Across the Financial Services Industry"

(2018).

All Faculty Scholarship

. 1740.

https://scholarship.law.upenn.edu/faculty_scholarship/1740

This Article is brought to you for free and open access by the Faculty Works at Penn Carey Law: Legal Scholarship

Repository. It has been accepted for inclusion in All Faculty Scholarship by an authorized administrator of Penn

Carey Law: Legal Scholarship Repository. For more information, please contact biddlerepos@law.upenn.edu.

BAKERDELLAERT_PP_FINAL(DO NOT DELETE) 1/7/2018 11:38 PM

713

Regulating Robo Advice Across the

Financial Services Industry

Tom Baker & Benedict Dellaert

ABSTRACT: Automated financial product advisors—“robo advisors”—are

emerging across the financial services industry, helping consumers choose

investments, banking products, and insurance policies. Robo advisors have

the potential to lower the cost and increase the quality and transparency of

financial advice for consumers. But they also pose significant new challenges

for regulators who are accustomed to assessing human intermediaries. A well-

designed robo advisor will be honest and competent, and it will recommend

only suitable products. Because humans design and implement robo advisors,

however, honesty, competence, and suitability cannot simply be assumed.

Moreover, robo advisors pose new scale risks that are different in kind from

the risks involved in assessing the conduct of thousands of individual actors.

This Essay identifies the core components of robo advisors, key questions that

regulators need to be able to answer about them, and the capacities that

regulators need to develop in order to answer those questions. The benefits to

developing these capacities almost certainly exceed the costs, because the same

returns to scale that make an automated advisor so cost-effective lead to

similar returns to scale in assessing the quality of automated advisors.

INTRODUCTION ............................................................................. 714

II.ROBO ADVISORS AND FINANCIAL PRODUCT INTERMEDIARY

REGULATION ................................................................................. 719

A. POLICY JUSTIFICATIONS AND REGULATORY OBJECTIVES ............. 721

B. ROBO ADVISORS: COMPETENCE, HONESTY, AND SUITABILITY .... 724

1. A Health Insurance Robo Advisor ............................... 725

i. Competence ................................................................ 725

Baker is William Maul Measey Professor at the University of Pennsylvania Law School.

Dellaert is Professor, Department of Business Economics, Marketing Section, School of

Economics, Erasmus University Rotterdam. Baker is a co-founder of Picwell, a data analytics

company that makes insurance robo advisors, and Dellaert is a member of the board of

supervisors (Raad van Toezicht) of Independer.nl, the largest on-line insurance broker in the

Netherlands. Thanks to Grace Knofczynski and Luman Yu for helpful research assistance.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

714 IOWA LAW REVIEW [Vol. 103:713

ii.

 Honesty ..................................................................... 725

iii. Suitability .................................................................. 727

2. A Home Mortgage Robo Advisor ................................. 727

i. Competence and Suitability ........................................ 727

ii. Honesty ..................................................................... 728

3. An Investment Robo Advisor ........................................ 729

i. Competence ................................................................ 729

ii. Honesty ..................................................................... 731

iii. Suitability .................................................................. 731

III.ROBO ADVISORS: NEW REGULATORY CHALLENGES ...................... 732

A. COMPONENTS OF ROBO ADVISORS THAT POSE REGULATORY

CHALLENGES .......................................................................... 733

1. Ranking or Matching Algorithms and Processes ........ 734

2. Customer and Product Data ......................................... 737

3. Choice Architecture ...................................................... 739

4. Information Technology Infrastructure ...................... 741

B. SCALE AND THE CONCEPT OF A REGULATORY TRAJECTORY ........ 742

IV.CONCLUSION: BEYOND BASIC HONESTY, COMPETENCE, AND

SUITABILITY ................................................................................... 746

I. INTRODUCTION

The growth of investment robo advisors, web-based insurance exchanges,

online credit comparison sites, and automated personal financial

management services creates significant opportunities and risks that

regulators across the financial services spectrum have yet to systematically

assess, let alone address. Because of the scale that automation makes possible,

these services have the potential to provide higher quality and more

transparent financial advice to more people at lower cost than human

financial advisors.

However, this potential hardly guarantees that it will be

realized.

1. See FIN. CONDUCT AUTH., FINANCIAL ADVICE MARKET REVIEW 39 (2016),

https://www.fca.org.uk/publication/corporate/famr-final-report.pdf (encouraging U.K. financial

services regulators to take steps to promote the development of automated financial advice to

increase access to financial advice); infra Part III (discussing the cost-effective structure and

components of robo advisors and the unique challenges regulators face); cf. F

IN. INDUS.

REGULATORY AUTH., REPORT ON DIGITAL INVESTMENT ADVICE 8–9 (2016), http://www.finra.org/

sites/default/files/digital-investment-advice-report.pdf [hereinafter FINRA] (listing many good

governance practices for FINRA members to employ in relation to digital investment advisors, all or

most of which could also form the basis for external evaluation). See generally Abhijeet Sinha, White

Paper: Increasing the Efficiency and Effectiveness of Financial Advice with Robo-Advisors,

INFOSYS (2016),

https://www.infosys.com/industries/financial-services/

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 715

Indeed, the emergence of robo advice does not dispense with the role

people play in the industry. People design, model, program, implement, and

market these automated advisors, and many automated advisors operate

behind the scenes, assisting people who interact with clients and customers.

The history of people taking advantage of consumers in the financial services

industry is not a pretty one.

Setting aside fraud and other unsavory activities,

the riches to be won by disrupting the financial services industry provide more

than enough incentive to rush technology to market.

In addition, there are

concerns that automation may entrench historical unfairness

and promote a

financial services monoculture with new kinds of unfairness and a greater

vulnerability to catastrophic failure than the less coordinated actions of

humans working without automated advice.

The challenges automated advice pose to regulators seeking to preserve

the integrity of financial markets do not stop there. There are well-known

privacy and security challenges that accompany the digitization of personal

financial data,

and new regulatory challenges that are more specific to

white-papers/Documents/trend-financial-advisors-industry.pdf (demonstrating that investors can

gain advice from robo advisors at much cheaper costs than the fees charged by human advisors).

2. See, e.g., Daniel R. Fischel & Robert S. Stillman, The Law and Economics of Vanishing Premium

Life Insurance, 22

DEL. J. CORP. L. 1, 1–3 (1997) (describing the vanishing premium scandal in the life

insurance industry in the early 1990s); Neil Fligstein & Alexander F. Roehrkasse, The Causes of Fraud in

the Financial Crisis of 2007 to 2009: Evidence from the Mortgage-Backed Securities Industry, 81 A

M. SOC. REV.

617, 617 (2016); Michael Corkery, Wells Fargo Fined $185 Million for Fraudulently Opening Accounts, N.Y.

TIMES (Sept. 8, 2016), http://www.nytimes.com/2016/09/09/business/dealbook/wells-fargo-fined-

for-years-of-harm-to-customers.html; Matthias Rieker, Broker Ordered to Pay More Than $1 Million in

Churning Case, W

ALL ST. J. (Oct. 13, 2014, 4:24 PM), http://www.wsj.com/articles/broker-ordered-to-

pay-more-than-1-million-in-churning-case-1413231863.

3. See Thomas Philippon, The FinTech Opportunity 14 (Nat’l Bureau of Econ. Research,

Working Paper No. 22476, 2016), http://www.nber.org/papers/w22476.pdf (discussing

disruptive innovations of Fintech startups).

4. See, e.g., W

ENDELL WALLACH & COLIN ALLEN, MORAL MACHINES: TEACHING ROBOTS RIGHT

FROM

WRONG 55–56 (2009) (advocating to ensure that autonomous artificial agents are created with

morality); Solon Barocas & Andrew D. Selbst, Big Data’s Disparate Impact, 104 C

ALIF. L. REV. 671, 677

(2016) (describing discriminatory effects of data mining); Joshua A. Kroll et al., Accountable Algorithms,

165 U.

PA. L. REV. 633, 637–38 (2017) (noting the challenges algorithms pose for procedural

regularity); Kate Crawford, Artificial Intelligence’s White Guy Problem, N.Y.

TIMES (June 25, 2016), http://

www.nytimes.com/2016/06/26/opinion/sunday/artificial-intelligences-white-guy-problem.html

(“Sexism, racism and other forms of discrimination are being built into the machine-learning

algorithms that underlie the technology behind many ‘intelligent’ systems . . . .”).

5. See generally,

CATHY O’NEIL, WEAPONS OF MATH DESTRUCTION: HOW BIG DATA INCREASES

INEQUALITY AND THREATENS DEMOCRACY (2016) (outlining dangers of relying on data analytics);

Dario Amodei et al., Concrete Problems in AI Safety (July 25, 2016) (unpublished manuscript),

https://arxiv.org/pdf/1606.06565v2.pdf (discussing “accident risk” that may emerge from the

poor design of the real-world AI systems). For an effort by the tech industry to address some of these

challenges, see P

ARTNERSHIP ON AI, https://www.partnershiponai.org (last visited Oct. 29, 2017).

6. See, e.g., Rick Swedloff, Risk Classification’s Big Data (R)evolution, 21 C

ONN. INS. L.J. 339,

339 (2014) (noting that “big data raises novel privacy concerns”); Press Release, N.Y. State Dep’t

of Fin. Servs., Governor Cuomo Announces Proposal of First-in-the-Nation Cybersecurity

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

716 IOWA LAW REVIEW [Vol. 103:713

automated advice. These include developing the capacities to assess: the

algorithms and data incorporated in the automated advisors; choice

architecture through which the advice is presented and acted upon;

underlying information technology infrastructures; and downside risk from

the scale that automation makes possible. Developing these capacities will

require financial service authorities—the paradigmatic expert administrative

agencies—to invest in new kinds of expertise. Our research and experience

suggests that the areas of expertise needed include data science, computer

science, behavioral economics, and psychology, to name just a few.

The benefits to developing these capacities almost certainly exceed the

costs because the same returns to scale that make an automated advisor cost-

effective lead to similar returns to scale in assessing the quality of automated

advisors. An expert administrative agency is well situated to realize those

returns to scale. Moreover, the potential solvency and systemic risks posed by

hundreds of thousands, or even millions, of consumers choosing their

financial products based on the same or similar models are sufficiently large

and different in kind from those traditionally posed by consumer financial

product intermediaries that some regulatory attention is justified on those

grounds alone.

At the same time, however, it is important not to overreact by setting a

higher bar for automated advisors than for human advisors. For now, the

standard against which automated advisors should be compared is that of

humans, whom we know are much less than perfect.

Although a large body

of research in diverse fields demonstrates that even simple algorithms

regularly outperform humans in the kinds of tasks that robo advisors

perform,

and, thus, it may be appropriate to hold automated advisors to a

Regulation to Protect Consumers and Financial Institutions (Sept. 13, 2016), http://www.dfs.

ny.gov/about/press/pr1609131.htm.

7. See, e.g., Benedict G.C. Dellaert & Gerald Häubl, Searching in Choice Mode: Consumer

Decision Processes in Product Search with Recommendations, 49 J.

MARKETING RES. 277, 285–86 (2012);

Eric J. Johnson et al., Can Consumers Make Affordable Care Affordable? The Value of Choice Architecture,

8 PLOS ONE 1, 2 (2013) (studying impact of variations in choice architecture on health

insurance choice); Benedict Dellaert et al., Sorted Partitioned Sets as Personalized Choice

Architecture, (unpublished paper) (on file with author).

8. See infra Part III.

9. For a summary of research regarding the imperfections of human advisors see

Philippon, supra note 3, at 18.

10. See, e.g., Berkeley J. Dietvorst et al., Algorithm Aversion: People Erroneously Avoid Algorithms After

Seeing Them Err, 144 J.

EXPERIMENTAL PSYCHOL. 114, 118 (2014) (showing that algorithms

outperform human forecasters in future predictions); William M. Grove et al., Clinical Versus

Mechanical Prediction: A Meta-Analysis, 12 P

SYCHOL. ASSESSMENT 19, 25 (2000) (noting that

mechanical prediction is as accurate or more accurate than the clinical prediction); William M.

Grove & Paul E. Meehl, Comparative Efficiency of Informal (Subjective, Impressionistic) and Formal

(Mechanical, Algorithmic) Prediction Procedures: The Clinical–Statistical Controversy, 2 P

SYCHOL., PUB.

POL’Y, & L. 293, 299–315 (responding to commonly heard objections to algorithmic procedures).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 717

super-human standard someday, their market share is too small and regulators

have too much to learn to do so today.

Our goal in this Essay is to open a discussion within legal and financial

services scholarship that invites the participation of those with expertise in

other relevant disciplines. As automated advisors grow in scale, protecting the

integrity of financial markets will require the kind of cross-disciplinary

cooperation that regularly occurs in the domains of health and environmental

regulation. The lawyers, economists, and behavioral scientists already involved

in financial services regulation will need to understand enough about

computer and data science to craft and apply new regulatory strategies, and

the computer and data scientists at the forefront of the innovation will need

to understand enough about legal structures and ways of thinking to help

make the new regulatory strategies sensible.

This assessment is a pressing need for the financial services authorities,

and it also presents an opportunity to explore the challenges and

opportunities that automated advice presents more broadly.

This

opportunity arises from the substantial legal, economic, and historical

authority that financial services regulators already have to guide their actions,

along with an array of regulatory tools to employ.

Thus, automated

consumer financial product advice provides a good case study as automation

extends into consumer markets. Though not everything we learn from this

case study will apply in other contexts, such as automated advice about cars,

homes, and vacations, there are similar opportunities to take advantage of

consumers in these and other markets for complex goods and services. Thus,

consumer protection techniques that work for financial products are worth

considering in other contexts.

In the body of this Essay we first identify the aspects of current financial

services regulation that apply most directly to robo advice: the regulation of

intermediaries such as securities brokers, insurance agents, and mortgage

brokers.

We set out the traditional goals of that regulation: promoting

competence (to provide appropriate advice and associated services), honesty (of

that advice and associated services), and suitability (of the financial products

11. The estimated size of the retirement market in the U.S. is $25 trillion. INV. CO. INST.,

NVESTMENT COMPANY FACT BOOK 124 (2015), https://www.ici.org/pdf/2015_factbook.pdf. By

comparison, as of Q1 2017, the total assets under investment of the six largest robo advisors ranged

from a high of $60 billion (Vanguard Personal Advisor) to a low of $1 billion (Future Advisor, which

is owned by BlackRock). See Tom Baker & Benedict Dellaert, Regulating Robo Advisors: Old Policy Goals,

New Challenges, W

HARTON PUB. POL’Y ISSUE INITIATIVE, July 2017, at 1–2.

12. See, e.g., W

ALLACH & ALLEN, supra note 4, at 55–56; Barocas & Selbst, supra note 4, at 677;

Kroll et al., supra note 4, at 657 (noting the challenges algorithms pose for procedural regularity).

13. See generally M

ICHAEL S. BARR ET AL., FINANCIAL REGULATION: LAW AND POLICY (Robert

C. Clark et al. eds., 2016) (exploring law and policy in financial regulation).

14. See infra Part II.A.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

718 IOWA LAW REVIEW [Vol. 103:713

sold to, or recommended for, the specific consumer).

We then explain why

any well-designed robo advisor should meet those goals at least as well as a

typical human advisor, most likely better, with the emphasis appropriately

placed on the caveat, “well designed.” At the same time, however, robo advice

raises new challenges for regulators, most immediately to develop the

expertise to assess whether robo advisors in fact are well designed.

In beginning with these traditional goals, we have two objectives: first, to

review why robo advisors are at least potentially superior to unassisted humans on

these dimensions for most consumers; and, second, to create a conceptual link

between existing regulatory goals and the new regulatory concerns. That

conceptual link supports regulators’ efforts to proceed under their existing legal

authority to develop the capacities they need to address these new concerns,

recognizing that they will need to operationalize this authority in new ways.

We then identify the core technical components of robo advisors that

regulators need to understand and develop procedures to assess, including

the algorithms and processes that generate personalized rankings of financial

products for consumers; the consumer and financial product data that the

algorithms ingest; the choice architecture through which that advice is

delivered; and the associated information technology infrastructure. Focusing

on these core components is the first stage of a regulatory trajectory that

regulators can follow as robo advisors develop in sophistication and scale.

This analysis is conceptual and not specific to any specific governmental

agency, private regulatory organization, or ex post liability regime, nor is it

specific to any sector of the financial services market. At a conceptual level,

the analysis applies to most, if not all, consumer financial products and to

regulators of these products. Accordingly, we advocate an inter-governmental,

inter-agency dialog, with experts from inside and outside the financial services

15. See infra Part II.B. Note that this description of the three goals is a conceptual one that

does not map perfectly on the diversity of financial services regulations. Cf. Howell E. Jackson,

The Trilateral Dilemma in Financial Regulation, in O

VERCOMING THE SAVING SLUMP: HOW TO

NCREASE THE EFFECTIVENESS OF FINANCIAL EDUCATION AND SAVING PROGRAMS 82, 100

(Annamaria Lusardi ed., 2008) (“Generalizations are tricky given the range of legal regimes.”).

16. See, e.g., Kara M. Stein, Comm’r, U.S. Sec. & Exch. Comm’n, Remarks at Harvard Law

School’s Fidelity Guest Lecture Series: Surfing the Wave: Technology, Innovation, and Competition

(Nov. 9, 2015), https://www.sec.gov/news/speech/stein-2015-remarks-harvard-law-school.html.

The Commission is now challenged with thinking through what it means to regulate

a robo advisor. This concept did not even exist when most of the laws applicable to

investment advisers were drafted. Most of these laws are based on the idea of a

human investment adviser on the other end of the phone or sitting across the table

from you. . . . Clearly, if we want our markets to remain at the top, we need to

embrace innovation. And FinTech clearly promises some exciting advances. But, we

also need to be prepared to anticipate and ideally prevent problems before they

arise. Remaining competitive requires both market participants and regulators to

thoughtfully evolve with innovation, not react to it way after the fact.

Id.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 719

industry sharing information, identifying ways to make the necessary human

capital available to regulators, and developing an approach to regulating robo

advisors that increases the likelihood that they are honest and competent.

Some agencies have taken steps to learn about robo advice as part of their

larger efforts to engage with Financial Technology, or “FinTech,” but to date

they have done so largely within their own regulatory silos and within their

own countries.

There is no formal inter-agency coordination in the United

States, and international coordination is even less developed.

While there is

no evidence that this lack of oversight and coordination has caused harm yet,

it almost certainly will in the future, as the market simply cannot be counted

upon to be self-correcting when robo advisors grow in scale to the point that

they reshape financial product markets.

In concluding, we explore steps that authorities might take beyond

demanding a minimum level of competence and honesty. These include

provisional ideas about how financial services regulation could facilitate

quality-based competition and diversity among robo advisors to ensure the

performance of intermediaries who use robo advisors increasingly exceeds

that of their unassisted competitors. In addition, as regulators gain

confidence in their capacity to assess and monitor robo advisors, and as robo

advisors become a major force in the market, there may be less need for direct

regulation of the forms and features of consumer financial products. Of

course, these regulatory benefits will not occur automatically. As any robo

advisor entrepreneur can attest, innovation requires understanding,

assessment, coordination, and feedback.

II. R

OBO ADVISORS AND FINANCIAL PRODUCT INTERMEDIARY REGULATION

In the popular press, a “robo advisor” is an automated investment service,

most likely based in San Francisco, which competes with financial advisors by

claiming to offer equally good, if not better, financial advice and service at a

17. See, e.g., U.S. SEC. & EXCH. COMM’N, DIV. OF INV. MGMT., GUIDANCE UPDATE: NO.

2017–02 (2017), https://www.sec.gov/investment/im-guidance-2017-02.pdf; see also FINRA, supra

note 1, at 3 (discussing governance and supervision of such advice). See generally Ryan VanGrack et al.,

Senior Advisor, U.S. Sec. & Exch. Comm’n, FinTech Forum: The Evolving Financial Marketplace (Nov.

14, 2016, 9:02 AM), https://www.sec.gov/spotlight/fintech/transcript-111416.pdf (discussing the

opportunities and challenges of robo advice). We note that the SEC forum included a state government

official from Vermont. Id. at 76.

18. The U.S. CFPB has been closely following the “Project Innovate” of the U.K. FCA, but

to our knowledge, there have not been systematic efforts to share information in both directions.

19. While we cannot prove this point, a suggestive example comes from the impact of

structured financial products during the financial crisis. See generally Joshua Coval et al., The

Economics of Structured Finance, 23 J. E

CON. PERSP. 3 (2009) (exploring the role of structured

finance activities in the financial crisis).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

720 IOWA LAW REVIEW [Vol. 103:713

lower price.

We use the terms “robo advice” and “robo advisor” more broadly

to include the similar services emerging in other sectors of the financial

services industry, most significantly in insurance, but also in consumer

credit.

Using these terms so broadly emphasizes the similarities of these

automated advisors across the financial services spectrum and supports the

claim that regulators from the securities, banking, and insurance sectors need

to work together to assess them. The investment-focused robo advisors have

drawn the most attention from regulators,

but the promises and regulatory

concerns raised by investment robo advisors apply equally to their insurance

and banking counterparts.

Accordingly, we use the term “robo advisor” broadly to refer to any

automated service that ranks, or matches consumers to, financial products on

a personalized basis.

Typically, the firms that provide these tools to

consumers also sell the recommended financial products, but advertising

funded “lead generation” services also can provide them.

The consumer

20. See Rob Berger, 7 Robo Advisors That Make Investing Effortless, FORBES: INVESTING (Feb. 5, 2015,

10:28 AM), http://www.forbes.com/sites/robertberger/2015/02/05/7-robo-advisors-that-make-

investing-effortless (discussing several popular robo advisors and their key features); see also Arielle

O’Shea, Best Robo-Advisors: 2017 Top Picks, N

ERDWALLET (June 23, 2017), https://www.nerdwallet.

com/blog/investing/best-robo-advisors (defining a robo-advisor and describing its features).

21. Insurance robo advisors include Healthcare.gov (health insurance) and CoverHound.com

(auto and homeowners insurance). While we have not found any true robo advisors in the banking

context, Zillow’s mortgage comparison tools and NerdWallet’s credit card comparison tools are a step

in that direction.

22. See, e.g., B

LACKROCK, DIGITAL INVESTMENT ADVICE: ROBO ADVISORS COME OF AGE 1 (2016),

http://www.blackrock.com/corporate/en-lm/literature/whitepaper/viewpoint-digital-investment-

advice-september-2016.pdf;

FINRA, supra note 1, at 1; MASS. SEC. DIV., POLICY STATEMENT: ROBO-

DVISERS AND STATE INVESTMENT ADVISER REGISTRATION 4 (2016), http://www.sec.state.ma.us/sct/

sctpdf/Policy-Statement--Robo-Advisers-and-State-Investment-Adviser-Registration.pdf.

23. See, e.g., C

TRS. FOR MEDICARE & MEDICAID SERVS., PROCESSES AND GUIDELINES FOR BECOMING

WEB-BROKER IN THE FEDERALLY-FACILITATED MARKETPLACE 7 (2015), https://www.cms.

gov/CCIIO/Programs-and-Initiatives/Health-Insurance-Marketplaces/Downloads/AB-Task-23-Draft-

Web-broker-101-Webinar-Slide-Deck-09-30-15.pdf; Memorandum from the Ctr. for Consumer Info.

& Ins. Oversight 1 (Jan. 8, 2016), https://www.cms.gov/CCIIO/Programs-and-Initiatives/Health-

Insurance-Marketplaces/Downloads/Role-of-ABs-in-Marketplace-1_6_16.pdf.

24. Although it is more traditional in the legal literature to refer to mutual fund shares,

bank accounts, and insurance policies as financial services, rather than products, people in the

industry use the term “product,” and related terms like manufacturer, distributor, retailer, and

designer, because those terms allow for more precise description and analysis than the broader

and conceptually vague term “financial service.” See, e.g., E

RNST & YOUNG, US FUND DISTRIBUTION

2014: SEA OF CHANGE ON THE HORIZON 3 (2014) (referring to manufacturers and distributors of

mutual funds), http://www.ey.com/Publication/vwLUAssets/ey-the-state-of-us-fund-distribution-in-

2014/$File/ey-us-fund-distribution-report.pdf.

25. A lead generation service is a website that provides information to consumers about a product

and makes money by referring consumers to another company that actually sells the product. Examples

include: Zillow’s mortgage comparison tool and Credit Karma’s credit card comparison tool. See Best

Credit Cards from Our Partners, C

REDIT KARMA, https://www.creditkarma.com/credit-cards (last visited

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 721

products that could be the subject of such robo advice include: deposit

accounts, home mortgages, and other forms of consumer credit from the

banking sector; all of the personal lines of insurance, including auto, life,

disability, health, and homeowners insurance, as well as annuities; and, from

the securities sector, mutual fund shares and other savings products regulated

as securities.

Our definition of “robo advisor” is closely aligned with what the Financial

Industry Regulatory Authority (“FINRA”) calls “digital investment advice

tools,” which include automated investment analysis and recommendation

services sold to traditional financial advisors.

Our broader definition

includes both “hybrid robos,” which place a human interface on top of what

is primarily an automated process, and automated portfolio selection and

management tools sold to more traditional financial advisors who provide

services that retain even more of a human touch.

Like FINRA, we mean to

emphasize the continuities between the new, consumer-facing automated

intermediaries and the kinds of automated services that have previously been

available to traditional intermediaries.

We expand the term “robo advisor”

even further, however, to include comparably automated services in the

insurance and banking sectors to emphasize the technological and regulatory

continuities across the financial services industry.

A. P

OLICY JUSTIFICATIONS AND REGULATORY OBJECTIVES

In a nutshell, the primary justifications for consumer financial services

regulation are the following: to provide some degree of protection for

Oct. 29, 2017); Compare Mortgage Rates and Home Loans, ZILLOW, https://www.zillow.com/mortgage-

rates (last visited Oct. 29, 2017).

26. Securities issued by operating companies are not consumer financial products in the

sense in which we are using that term. If a financial product intermediary developed and mass

marketed a service that created individualized securities portfolios, we would regard those

portfolios as the consumer product, not the securities in the portfolios, unless consumer financial

product companies started creating securities designed to be included in those portfolios.

27. FINRA, supra note 1, at 2 (dividing digital investment advice tools into two groups:

“financial professional-facing” tools and “client-facing” tools).

28. See, e.g., F

RANCIS M. KINNIRY JR. ET AL., VANGUARD RES., PUTTING A VALUE ON YOUR VALUE:

QUANTIFYING VANGUARD ADVISOR’S ALPHA 16 (2016), http://www.vanguard.com/pdf/ISGQVAA.

pdf (noting the benefits that the Vanguard Advisor’s Alpha framework can provide for traditional

financial advisors); Efi Pylarinou, SigFig Advises Hundreds of Billions and Pivots from B2C to B2B, B

ANK

INNOVATION (Aug. 17, 2015), http://bankinnovation.net/2015/08/sigfig-advises-hundreds-of-

billions-and-pivots-from-b2c-to-b2b (discussing the success of a SigFig tool designed to provide

advice for independent brokers).

29. See FINRA, supra note 1, at 2–3 (describing history of digital investment advice); see also B

ARR

ET AL

., supra note 13, at 471 (automated investment services have been “used for many years by financial

professionals to develop a customer’s trading portfolio”); Michael S. Piwowar, Comm’r, U.S. Sec.

& Exch. Comm’n, Statement at Financial Technology Forum (Nov. 14, 2016), https://www.sec.gov/

news/statement/piwowar-statement-financial-technology-forum-111416.html (“FinTech is frequently

lauded as a disruptive force that is transforming the financial services industry . . . .”).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

722 IOWA LAW REVIEW [Vol. 103:713

consumers regarding the safety of their financial products; to protect society

from the negative consequences of the failure of financial product providers;

and to protect consumers from being exploited due to their relative lack of

knowledge about financial products and their dependence on the product

providers.

In addition, some financial services regulation aims to ameliorate

inequality, for example, by limiting the ability of insurance companies to

charge prices based on risk or requiring banks to provide services within

minority and low income communities.

Finally, some financial services

regulation aims to advance broader political economy goals, such as anti-trust

regulation, money laundering regulation, and regulations that discourage

financial companies from amassing too much political power.

The financial services industry and its associated regulatory bodies are

traditionally divided into three major sectors: securities, banking, and

insurance.

The relative importance of the policy justifications for regulation

differs across these sectors. For example, banking regulation attempts to make

ordinary consumer bank deposits completely safe through a combination of

solvency regulation and deposit insurance,

and insurance regulation

attempts to make most kinds of consumer insurance almost as completely safe

through solvency regulation and guaranty funds.

By contrast, securities

regulators do not attempt to make mutual funds or other securities

completely safe. They focus instead on increasing the transparency of

30. See Howell E. Jackson, Regulation in a Multisectored Financial Services Industry: An Exploratory

Essay, 77 W

ASH. U. L.Q. 319, 334–36 (1999). Note that we have simplified Jackson’s framework in

light of our focus on robo advisors. For example, we do not separately identify the objective of

protecting consumers from a financial services provider changing its risk profile after purchase.

31. See Tom Baker, Health Insurance, Risk, and Responsibility After the Patient Protection and

Affordable Care Act, 159 U.

PA. L. REV. 1577, 1589–91(2011) (explaining how the ACA restricts

the ability of health insurers to price based on risk). See generally Keith N. Hylton & Vincent D.

Rougeau, Lending Discrimination: Economic Theory, Econometric Evidence, and the Community

Reinvestment Act, 85 G

EO. L.J. 237 (1996) (explaining history of the Community Reinvestment

Act and testing the underlying market discrimination thesis).

32. Jackson, supra note 30, at 337; cf. Mark J. Roe, Foundations of Corporate Finance: The 1906

Pacification of the Insurance Industry, 93 C

OLUM. L. REV. 639, 639–41 (1993) (asserting the

influence of American politics on the determinacy of corporate structures); Press Release, Dep’t

of Fin. Servs., DFS Issues Final Anti-Terrorism Transaction Monitoring and Filtering Program

Regulation (June 30, 2016), http://www.dfs.ny.gov/about/press/pr1606301.htm (noting the

announcement of the anti-money laundering regulation by the New York State Department of

Financial Services).

33. Jackson, supra note 30, at 320. Jackson identifies futures trading and pensions as distinct

from ordinary securities transactions. See, e.g., id. at 362. We ignore futures trading and defined

benefit pensions, and we treat defined contribution pensions as securities transactions with an

employment law overlay.

34. Id. at 332–39.

35. Id. at 358. Note that in the insurance sector completely safe means only that the

insurance company has the financial capacity to pay claims, not that the insurance company will

have a perfect record in paying claims.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 723

securities markets, preventing fraud, and promoting education and research

that helps consumers manage investment risk.

In the financial services realm, regulation addresses solvency, entity

organization and licensing, market conduct, and product approval.

The

aspects of financial services regulation most likely to apply to robo advisors

are those directed at consumer product intermediaries.

These regulations

apply directly to the companies that create robo advisors when those

companies also function as consumer product intermediaries themselves, and

indirectly when they sell their robo advisor services to consumer product

intermediaries.

Within the schema of the policy justifications set out above,

protecting consumers from being taken advantage of due to their relative lack

of knowledge about financial products is most relevant to intermediaries. This

justification is important across all three major sectors of the financial services

industry.

Intermediaries like securities brokers, mortgage brokers, and insurance

agents and brokers all have the potential to help consumers make better sense

of the financial services available to them and, accordingly, to ameliorate the

information imbalance between consumers and the producers of financial

products.

However, there are significant challenges to reaching this

36. Jackson, supra note 30, at 345–46; Toni Williams, Empowerment of Whom and for What? Financial

Literacy Education and the New Regulation of Consumer Financial Services, 29 L

AW & POL’Y 226, 232 (2007)

(noting that national regulators in the UK and Canada represent financial education as empowering

consumers to manage investment risks); Investor Alert: Automated Investment Tools, U.S.

SEC. & EXCHANGE

COMMISSION (May 8, 2015), https://www.sec.gov/oiea/investor-alerts-bulletins/autolistingtools

htm.html (providing a general overview of automated investment tools for investors).

37. See generally B

ARR ET AL., supra note 13 (identifying and discussing agencies responsible

for licensing and entities and supervising market conduct in all three sectors, protecting solvency

in the insurance and banking sectors, and approving products in the insurance sector).

38. Note that different strands of academic literature use the term “intermediary”

differently. For example, in the financial services literature the term is used to refer to banks,

insurance companies, and mutual fund companies. See Jackson, supra note 30, at 328–30. By

contrast, the insurance and industrial organization literatures use the term to refer to

middlemen. See, e.g., Gary Biglaiser & James W. Friedman, Middlemen as Guarantors of Quality, 12

NT’L J. INDUS. ORG. 509, 509–12 (1994). We use the term to refer to middlemen and to the retail

sales aspects of banks, insurance companies and mutual fund companies. For a survey of

regulatory strategies directed at the retail sales and advising function, see generally Jackson, supra

note 15 (cataloging circumstances in which consumers rely on financial advisors to recommend

products or services and regulatory approaches to addressing the problem of steering based on

side payments to the advisors).

39. See FINRA, supra note 1, at 1.

40. See, e.g., J. David Cummins & Neil A. Doherty, The Economics of Insurance Intermediaries,

73 J.

RISK & INS. 359, 386 (identifying the role of insurance agents and brokers as information

intermediaries in reducing adverse selection); Daniel Schwarcz & Peter Siegelman, Insurance

Agents in the 21st Century: The Problem of Biased Advice, in R

ESEARCH HANDBOOK IN THE LAW

ECONOMICS OF INSURANCE 36, 41–43 (Daniel Schwarcz & Peter Siegelman eds., 2015)

(reviewing research on other kinds of intermediaries, such as financial advisors).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

724 IOWA LAW REVIEW [Vol. 103:713

potential.

Consumers are almost as poorly equipped to identify the quality

of an intermediary as they are to evaluate the quality of the financial

products.

For example, because they need the help of the intermediary to

evaluate those products, they cannot evaluate the quality of the intermediary

by evaluating the quality of the intermediary’s recommendations. Moreover,

the prevailing commission-based compensation for intermediaries creates

significant conflicts of interests that lead to biased advice.

Finally, the

diversity and complexity of financial products makes it difficult to be

sufficiently expert to consistently offer good advice, especially across the

range of financial services.

Financial services regulation addresses these challenges using three main

sets of regulatory tools: licensing and education requirements designed to

ensure that an intermediary has at least a minimum level of competence

regarding the products the intermediary is licensed to sell; disclosure

requirements and antifraud rules that require intermediaries to be honest

with their customers; and standards of conduct, such as the fiduciary standard,

designed to encourage intermediaries to match their customers with suitable

financial services.

These regulatory tools already apply to robo advisors

indirectly, when regulated entities provide robo advice. However, it is

important to consider whether regulators can and should apply any of these

tools directly to the robo advisors themselves, and not just to the currently

licensed and regulated entities that use them.

B. R

OBO ADVISORS: COMPETENCE, HONESTY, AND SUITABILITY

We contend that, at least for mass-market consumer financial products,

a well-designed robo advisor will be more competent and suitable than most

human advisors while being as honest as the most honest humans. We

recognize that, by specifying that the advisor be well designed, we are stacking

the deck in favor of robo advisors. At this point, our goal is simply to

41. Jackson, supra note 15, at 83–84.

42. See Schwarcz & Siegelman, supra note 40, at 47–48 tbl.2.1 (summarizing empirical

studies showing “financial intermediaries serving ordinary consumers often offer poor advice”).

43. See C

OUNCIL OF ECON. ADVISERS, THE EFFECTS OF CONFLICTED INVESTMENT ADVICE ON

RETIREMENT SAVINGS 13, 15–16 (2015), http://purl.fdlp.gov/GPO/gpo55500 (identifying

several ways in which conflicts of interest affect the quality of advice and the subsequent

investment performance); Schwarcz & Siegelman, supra note 40, at 44–47 (describing the effect

of intermediaries’ commission-based compensation).

44. See N

AT’L ASS’N OF INS. COMM’RS, REVISIONS AND CLARIFICATIONS TO THE UNIFORM LICENSING

STANDARDS (2011), http://www.naic.org/documents/committees_ex_pltf_producer_licensing_ul_

standards_revised.pdf (describing education requirements); Jackson, supra note 30, at 344–45,

349–51 (describing disclosure and anti-fraud rules and standards of conduct); Schwarcz & Siegelman,

supra note 40, at 61–62 (describing licensing requirements). Note that we simplify Jackson’s more

detailed list of regulatory strategies set out in Jackson, supra note 15, at 100 (noting that

“generalizations are tricky given the range of legal regimes”). For present purposes we include price

controls and prohibitions on certain conduct within the general category of standards of conduct.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 725

demonstrate why it is plausible that a robo advisor could be well designed.

The examples that follow lay the groundwork for our subsequent exploration

of what is involved in assessing whether a robo advisor is well-designed.

1. A Health Insurance Robo Advisor

Our first example is the one with which we have the most experience:

health insurance robo advisors.

i. Competence

In this context, competence means the ability to help consumers select a

health insurance plan that provides appropriate coverage at a reasonable cost,

taking into account factors such as consumers’ risk aversion with regard to

out-of-pocket payments and their preferences about qualitative differences

among the plans, such as the network of health care providers and the extent

of the health plans’ involvement in managing care.

ii. Honesty

Honesty is somewhat harder to define in this and other contexts. In our

view, honesty includes making only true statements about the health plans,

the intermediary’s compensation, and the purchase process. In addition,

honesty should include accurately describing the basis for any

recommendations, making disclosures to correct a misimpression that the

advisor is considering all of the plans in the market if the advisor is not doing

so, disclosing any compensation or other arrangements that might have the

potential to bias the advice adverse to the consumer’s interests, and providing

advice that is not in fact biased in that manner.

A well-designed health insurance robo advisor should provide advice that

is superior to humans working without automated assistance in terms of both

competence and honesty. Even an unbiased and competent human advisor

can at best offer rules of thumb based on the choice patterns that the advisor

has observed or learned about. For example, an experienced broker could

offer advice such as the following: when choosing among the four plans

45. See, e.g., AON RETIREE HEALTH EXCHANGE, https://retiree.aon.com (last visited Oct. 29,

2017). For recent evidence that consumers need this kind of advice even to pick among a small

set of options, see generally Saurabh Bhargava, George Loewenstein & Justin Sydnor, Do

Individuals Make Sensible Health Insurance Decisions? Evidence from a Menu with Dominated Options,

(Nat’l Bureau of Econ. Research, Working Paper No. 21160, 2015) (demonstrating that people

made objectively bad decisions when choosing among a small number of health insurance plans).

If the company that creates the robo advisor also functions as an insurance broker, the company

will also need to be competent at brokers’ other functions, such as making sure that the insurance

policy is issued in a timely manner and that consumers’ ongoing customer service needs are met.

These latter functions are obviously very important to consumers, but they are not part of the “robo

advice” we are exploring in this essay, nor do they lie at the core of the comparative advantage of

health insurance robo advisors. Accordingly, we will set them aside for present purposes.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

726 IOWA LAW REVIEW [Vol. 103:713

typically offered at a major U.S. university, the expensive, low-deductible,

broad-network plan is the one preferred by the professors and the doctors at

the university hospital; the cheaper HMO plan is preferred by the staff and

the healthy, young assistant professors.

Even when such rules of thumb are

correct on average, they will provide the wrong guidance for some people,

and they are quite unlikely to be helpful when the alternatives expand beyond

a small number of options.

Health insurance robo advisors can offer richer, more personalized

advice. With access to the right data and the ability to ask a few questions of

the consumer, they can develop reliable predictions of the likely range of costs

under all of the available plans; they can create personalized rankings that use

survey data, expert assessments and other techniques to take into account

both price and non-price features of the plans; they can customize these

rankings based on the risk aversion and other expressed preferences of

individual consumers; and they can do all of this instantaneously with choice

sets of any size.

In terms of honesty, a robo advisor will always provide the advice that it

is programmed to provide, and it can be programmed in a way that meets a

demanding standard of honesty: making only true statements, disclosing the

methods for providing the advice, and providing advice that takes into

account only factors that are consistent with the consumer’s interests (insofar

as it is possible to know those interests). Indeed, this more demanding honesty

standard should be considered an aspect of what it means to be well-designed.

While human advisors can endeavor to meet this same standard of honesty,

common sense and psychological research make it hard to imagine people

always meeting that standard when they are rewarded in ways that are not fully

aligned with consumer interests.

46. See generally Pavel Atanasov & Tom Baker, Putting Health Back into Health Insurance Choice,

71 M

ED. CARE RES. & REV. 337 (2014) (describing plans offered at a large private university).

47. In our university example, there will be some senior faculty who would be well served by

a high deductible plan with an HSA or an HMO, and who would appreciate having a few thousand

more dollars each year to spend on other things, and there also are likely to be some staff and

healthy junior faculty whose preferences for freedom to choose doctors and distaste for both

deductibles and HMOs are sufficient to justify paying the higher price for the low deductible,

broad network plan. See id. at 338.

48. B

ARRY SCHWARTZ, THE PARADOX OF CHOICE: WHY MORE IS LESS 2 (2004) (“[A]s the number

of choices keeps growing, negative aspects of having a multitude of options begin to appear. As the

number of choices grows further, the negatives escalate until we become overloaded.”).

49. See B

EN HANDEL & JONATHAN KOLSTAD, HAMILTON PROJECT, GETTING THE MOST FROM

MARKETPLACES: SMART POLICIES ON HEALTH INSURANCE CHOICE 12 (2015), http://www.hamilton

project.org/assets/files/smart_policies_on_health_insurance_choice_final_proposal.pdf.

50. See, e.g., J

ONATHAN BARON, THINKING AND DECIDING (4th ed. 1988).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 727

iii. Suitability

Finally, the assessment of the competence of a robo advisor and the

suitability of the products it recommends go hand in hand. By definition, a

competent robo advisor will only recommend suitable products. Indeed,

using difficult test cases to evaluate whether a robo advisor consistently

recommends suitable products is one of the ways to evaluate whether the robo

advisor is well-designed. Humans, however, are different. Even competent

humans make mistakes, and competent humans can also be biased or

dishonest.

Indeed, it is for this reason that financial services regulators

developed suitability and other conduct standards that permit an after-the-

fact assessment of whether intermediaries gave good advice.

2. A Home Mortgage Robo Advisor

Our second example is a home mortgage robo advisor.

i. Competence and Suitability

In this context, competence means the ability to help consumers select

the right mortgage at the best rate available.

Doing this competently

requires taking the relevant details of the consumers’ financial situation into

account, including likely household income over time, amount and timing of

household financial obligations, risk factors associated with the kind of

mortgage in question, the likely length of time before sale of the home, the

consumer’s credit rating, and other factors that people with more domain

51. On competent humans making mistakes, see COMM. ON QUALITY OF HEALTH CARE IN

AM., INST. OF MED., TO ERR IS HUMAN: BUILDING A SAFER HEALTH SYSTEM 63–65 (Linda T. Kohn

et al. eds., 2000) (documenting the extent of medical malpractice and attempting to de-politicize

that issue by emphasizing that humans in all walks of life make mistakes). On competent humans

being biased, see generally Sendhil Mullainathan et al., The Market for Financial Advice: An Audit

Study (Nat’l Bureau Econ. Research, Working Paper No. 17929, 2012) (noting that financial

advisers tend not to de-bias their clients and instead often reinforce biases that are in their

interests). On competent humans being dishonest, see Nina Mazar et al., The Dishonesty of Honest

People: A Theory of Self-Concept Maintenance, 45 J.

MARKETING RES. 633, 642 (2008).

52. See Robert H. Mundheim, Professional Responsibilities of Broker-Dealers: The Suitability Doctrine,

1965 D

UKE. L.J. 445, 447–52 (presenting the justifications of and need for a suitability doctrine).

53. We have been unable to find any mortgage robo advisors. The closest service we have

found is Zillow’s mortgage rate comparison feature, which is an advertising and lead generation

tool that does not attempt to match the mortgage types to the consumer’s needs. See Compare

Mortgage Rates and Home Loans, supra note 25 (“We make it easy to see current mortgage rates,

compare multiple mortgage quotes, and sort results to find the lowest fees, APR, or monthly

payment.”); see also M

AGNIFYMONEY, http://www.magnifymoney.com (last visited Oct. 29, 2017)

(an example of an additional financial loan lead-generation service).

54. For a description of how difficult this is to do well because of risk-based mortgage

pricing, see Patricia A. McCoy, Rethinking Disclosure in a World of Risk-Based Pricing, 44 H

ARV. J. ON

LEGIS. 123, 138–47 (2007) (comparing transparency of pricing and terms in the prime market

with the subprime market).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

728 IOWA LAW REVIEW [Vol. 103:713

expertise than we can identify. As with health insurance, helping consumers

choose the best mortgage is a complicated matching problem that a well-

designed algorithm could execute on a more personalized, consistently

accurate basis than a human.

Additionally, because the assessment of

competence and suitability go hand in hand, a competent mortgage robo

advisor will only recommend suitable mortgages.

ii. Honesty

In the mortgage context, honesty means making only true statements

about a mortgage’s rates and features, the intermediary’s compensation, and

anything else relevant to the mortgage search, application, and closing

process. Honesty also includes accurately describing the basis for any

recommendations, disclosing the risks associated with taking out mortgages

in amounts or types that will be difficult for the consumer to afford, making

common sense disclosures that might be needed to correct a misimpression

regarding what plans the advisor is considering, disclosing the existence of

any compensation or other arrangements that have the potential to bias the

advice in a way that is not consistent with the consumer’s interests, and

providing advice that is not biased in that manner. As with health insurance,

a robo advisor can be programmed to meet this demanding standard of

honesty, and doing so should be considered an aspect of what it means to be

well-designed.

55. Cf. Howell E. Jackson & Laurie Burlingame, Kickbacks or Compensation: The Case of Yield

Spread Premiums, 12 S

TAN. J.L. BUS. & FIN. 289, 291–92 (2007) (demonstrating that when human

mortgage brokers are compensated through yield spread premiums they steer consumers to

mortgages with higher interest rates than optimal for consumers). A mortgage robo advisor could

be programmed to prevent this kind of mismatching.

56. If the company that creates the robo advisor also functions as a mortgage broker, it

would need to be competent at making sure that the mortgage is issued in a timely manner. The

full automation of that task is much more difficult—but also potentially much more valuable—

than fully automating the sale of insurance primarily because of the greater risk inherent in

issuing a mortgage. An insurance policy only obligates the insurer to pay costs in the future,

providing insurers the opportunity to police fraud in the sales process after the fact. By contrast,

a mortgage company gives the consumer the money up front in return for the consumer’s

promise to repay the money in the future. Automating that process would be valuable and

potentially part of the comparative advantage of the kind of company that would create a home

mortgage robo advisor. Nevertheless, we will not address it further, except to make two quick

observations. First, because a mortgage issuer gives the consumer the money up front, mortgage

issuers will not agree to issue mortgages on an automated basis at scale without verifying the

competence of that automation. Thus, the potential need for regulatory oversight of this aspect

of automation is less acute than that of the product matching function. Second, the immediate

beneficiaries of fully automated mortgage sales platforms are likely to be consumers with stable

relationships with banks, credit cards, employers, and other large institutions. Thus, full

automation has the potential to raise concerns about the inequality reproducing aspects of the

financial services industry.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 729

3. An Investment Robo Advisor

Our third example is the one most commonly associated with the label

“robo advisor.”

i. Competence

We consider investment robo advisors last, despite the fact that, arguably,

they are the most fully developed financial services robo advisors currently in

the market.

We leave them for last because it is most obvious to us that the

human financial advisors they are assisting or replacing need to be competent

at many things other than simply matching consumers to products. By

contrast, matching consumers to the relevant financial products is the core

function of insurance and mortgage brokers. Thus, as consumers come to

trust the speed and reliability of robo advisors’ ranking and matching

algorithms and the “always on” nature of automated services, and as the

logistical difficulties of automating the mortgage underwriting process are

overcome, it is easy to imagine automated systems, supplemented perhaps by

call centers, gradually replacing many of the human insurance and mortgage

brokers in the consumer market, as has already happened to travel agents.

By contrast, matching consumers to financial products is only part of

what a financial advisor can do for clients. A financial advisor can help people

decide how much and how best to save, and, when the time comes, how much

can safely be spent from those savings. Financial advisors can help clients

create plans; they can set up structures and processes for implementing and

sticking to plans; they can counsel clients who do not stick to the plans; and

they can help clients adjust plans when circumstances change. These are in

addition to the more readily automated tasks such as helping consumers

decide how much they should be saving for retirement in light of relevant

considerations regarding their financial situation, providing good advice

about how and when to change or rebalance their investments over time, and

providing projections to guide the retirement and decumulation process,

once again in light of relevant considerations regarding clients’ financial

situation over time.

57. See, e.g., U.S. SEC. & EXCH. COMM’N, supra note 17, at 1.

58. For examples of investment robo advisors, see the sources cited supra note 1.

59. See Shea Laverty, Impact of Technology on the Travel Agency Business, C

HRON, http://small

business.chron.com/impact-technology-travel-agency-business-57750.html (last visited Oct. 29, 2017)

(highlighting how technology has changed the travel agency business). We distinguish between forms

of insurance that, because of legal or contractual requirements, are not optional for consumers (e.g.,

auto and homeowners) and forms of insurance that are optional (e.g., life and disability insurance).

For the latter forms of insurance, persuading people to buy them is also a core function, one that we

expect will increasingly be the province of general purpose financial advisors and subject to automation

through that channel.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

730 IOWA LAW REVIEW [Vol. 103:713

Many financial advisors focus their efforts on selling financial products

that generate commissions and fees, rather than on planning and coaching,

and this helps explain why regulators and others have focused on the

incentives that can distort the matching function of financial advisors.

Yet,

planning and coaching can be more important than matching customers to

products. For example, a financial advisor who exclusively recommends

financial products that spin off high fees and commissions, but is good at

getting her clients to adopt routines that allow them to live within their means

and save and, consequently, to feel financially secure, may be helping her

clients more than the financial advisor who scrupulously recommends

optimal investments but is hopeless at helping clients to save and live within

their means.

As existing investment robo advisors demonstrate, designers can easily

automate product matching and other aspects of investing, such as

rebalancing,

for investors who are prepared to adopt the passive investing

strategy recommended by disinterested finance researchers.

Relationship

management and other, more difficult to model aspects of the work of

financial advisors are harder to automate. Accordingly, the robo advisors with

the largest and fastest growing market shares sell their services through or to

human financial advisors.

For example, the two largest sellers of passive

investment funds in the United States—Vanguard and BlackRock—both now

offer “hybrid robo” services that place a human relationship layer on top of a

highly automated process, for which consumers pay a management fee that is

somewhat higher than the fee for the fully automated robo advisors, but much

less than half of the fee of traditional financial advisors.

As robo advisors

60. COUNCIL OF ECON. ADVISERS, supra note 43, at 15–16.

61. Passive investing refers to investing in funds that attempt simply to match the

performance of the class of securities to which the fund is indexed. At present, investment robo

advisors often employ algorithms to match consumers to a mix of exchange traded (index) funds

based on the consumers’ age, risk tolerance, and time horizon, among other factors. Rebalancing

is the process of periodically adjusting the mix of investments so that differences in the relative

performance of the investments do not lead the investor’s portfolio to shift away from the

preferred mix. For a description of what it would mean for an investment robo advisor to be well

designed, see FINRA, supra note 1, at 8–9.

62. See, e.g., B

URTON G. MALKIEL, A RANDOM WALK DOWN WALL STREET: THE TIME-TESTED

STRATEGY FOR SUCCESSFUL INVESTING 288 (11th ed. 2016).

63. Peggy Collins & Charles Stein, The Vanguard Cyborg Takeover, B

LOOMBERG (Mar. 24,

2016, 11:00 AM), http://www.bloomberg.com/news/articles/2016-03-24/the-vanguard-cyborg-

takeover (noting that the Vanguard Group’s hybrid robo advisor has surpassed standalone robo

advisors); Vanguard and Schwab Hybrids Trump Pure Robos in Assets, F

IN. ADVISOR IQ (Mar. 29,

2016), https://financialadvisoriq.com/c/1323523/147603 (discussing the success of Vanguard

Group’s and Schwab’s hybrid models over the standalone robo advisors).

64. Clint Boulton, Roboadvisors Stand at the Vanguard of Human-Machine Collaboration, CIO (Mar.

25, 2016, 12:02 PM), http://www.cio.com/article/3048318/vertical-industries/roboadvisors-stand-at-

the-vanguard-of-human-machine-collaboration.html (describing Vanguard Group’s hybrid services);

With FutureAdvisor, BlackRock Seeks to Compete with Schwab, Vanguard, T

HINKADVISOR (June 14, 2016),

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 731

gradually replace the product matching function and other functions that are

easily automated, it is possible that in the retail consumer market, financial

advisors will largely replace stock brokers and that financial advisors

increasingly will compete on the basis of their ability to do more of the

planning and coaching aspects of their job.

We would applaud such a result,

and we are sufficiently confident that it will take place that we have begun

advising undergraduates who express an interest in helping professions such

as nursing and social work to also consider careers in financial planning.

ii. Honesty

As in the health insurance context, there are different potential

standards of honesty. At a minimum in our opinion, honesty means making

only true statements about the products, the advisor’s compensation, and

anything else that is relevant to the products, the advice, and the purchase

process, and honesty should also include accurately describing the basis for

any recommendations, making any common sense disclosures that might be

needed to correct a misimpression that the advisor is considering all of the

products in the market if the advisor is not doing so, disclosing the existence

of any compensation or other arrangements that might have the potential to

bias the advice in a way that is not consistent with consumer’s interests, and

providing advice that is not biased in that manner.

iii. Suitability

As with insurance and banking robo advisors, suitability goes hand in

hand with competence for an investment robo advisor. A competent

investment robo advisor will only recommend or make investments that are

suitable for the consumer who is relying on the robo advisor, whether directly

or indirectly.

http://www.thinkadvisor.com/2016/06/14/with-futureadvisor-blackrock-seeks-to-compete-with

(noting that BlackRock acquired FutureAdvisor to develop its hybrid services).

65. See Ryan VanGrack et al., supra note 17, at 12 (“[D]igital allows you to scale the service

model of the existing financial advisory ecosystem by taking some of the workload off of financial

advisors so they can focus on the unique differentiation and unique value added in terms of

coaching, [and] relationship building with their clients . . . .”). See generally David Dubofsky & Lyle

Sussman, The Changing Role of the Financial Planner Part 1: From Financial Analytics to Coaching and

Life Planning, 22 J.

FIN. PLAN. 48 (2009) (discussing similar changes for financial planners).

66. See U.S.

SEC. & EXCH. COMM’N, supra note 17, at 3–5 (listing disclosures that robo

advisers might make).

67. See id. at 7. The SEC’s recent Investment Management Guidance Update for robo advisors

raises the interesting complication of a robo advisor that gives clients the opportunity to choose

investments that the robo advisor did not recommend and, thus, that may not be suitable for the

investor according to the algorithm in the robo adviser. See id. The guidance document concludes:

[A] robo-adviser should consider providing commentary as to why it believes

particular portfolios may be more appropriate for a given investment objective and

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

732 IOWA LAW REVIEW [Vol. 103:713

III. R

OBO ADVISORS: NEW REGULATORY CHALLENGES

The discussion so far suggests that a well-designed robo advisor will be

competent and honest, and it will recommend to a consumer only financial

products that are suitable for that consumer. Of course, by specifying that the

robo advisors will be “well designed,” we have stacked the deck in their favor.

This is not because we believe that robo advisors necessarily will be well

designed. Indeed, we believe the contrary.

While robo advisors have the potential to outperform humans in

matching consumers to mass market financial products, they are not

inherently immune from the misalignment of incentives that has historically

affected financial product intermediaries.

A robo advisor can be designed

to ignore those incentives, but many consumer financial product

intermediaries that develop or purchase robo advisors are subject to those

incentives.

It would be naïve to simply assume that intermediaries will always

choose the algorithms and choice architecture that are best for consumers,

rather than those that are best for the intermediaries.

This means that regulators should take a more active role in assessing

robo advisors as robo advisors grow in scale. Indeed, the same returns to scale

that make a robo advisor cost-effective lead to similar returns to scale in

assessing the quality of a robo advisor. An expert administrative agency is well

situated to realize those returns to scale. Moreover, the potential solvency and

systemic risks posed by hundreds of thousands, or even millions, of consumers

choosing their financial products based on the same or similar models are

sufficiently large and different in kind from those traditionally posed by

risk profile. In this regard, a robo-adviser may wish to consider whether pop-up boxes

or other design features would be useful to alert a client of potential inconsistencies

between the client’s stated objective and the selected portfolio.

Id.

68. For example, our research and that of our collaborator Eric Johnson demonstrate how

simple choice architecture techniques can be used to mislead consumers, especially when

combined with a biased or inaccurate ranking algorithm. See generally Benedict G.C. Dellaert,

Tom Baker & Eric J. Johnson, Sorted Partitioned Sets as Personalized Choice Architecture,

(unpublished manuscript) (on file with author) (presenting results of experiment in which changing

the order of the presentation of plans changed consumer choices); see also generally Peter A. Ubel, David

A. Comerford & Eric Johnson, Healthcare.gov 3.0—Behavioral Economics and Insurance Exchanges, 372

EW ENG. J. MED. 695 (2015) (presenting results of an experiment in which switching the metals

assigned to plans changed consumer choices); Harris Meyer, Copycat Enrollment Websites Hamper ACA

Sign-up Efforts, M

OD. HEALTHCARE (Oct. 25, 2016), http://www.modernhealthcare.com/article/

20161025/NEWS/161029946/copycat-enrollment-websites-hamper-aca-sign-up-efforts (discussing

the impact of misleading enrollment sites on health insurance enrollment through healthcare.gov).

69. See Fligstein & Roehrkasse, supra note 2, at 625; Schwarcz & Siegelman, supra note 40,

at 64–66.

70. See, e.g., Schwarcz & Siegelman, supra note 40, at 625.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 733

consumer financial product intermediaries to justify regulatory attention on

those grounds alone.

The smart thing for regulators to do is to start developing the necessary

capacities now, when the stakes are lower, and when consumers are still

sufficiently uncertain about robo advisors that some firms may actually

welcome the legitimation that could accompany independent certification of

the quality of robo advice.

Indeed, we predict that at least some powerful

actors in the financial services sector will decide to support such regulatory

initiatives in order to be in a position to shape them in a manner that they

believe is sensible, as the largest asset management company in the United

States has already signaled that it is prepared to do.

Toward that end, we offer two sets of conceptually framing ideas. First,

based on our investigation of existing robo advisors across the three main

sectors of the financial services industry, we have identified four core

components of robo advisors that require distinct capacities to assess: (1) the

ranking or matching algorithms and related processes; (2) the customer and

financial product data to which the algorithms or other matching processes

are applied; (3) the choice architecture through which the advice is delivered;

and (4) the information technology infrastructure. Second, because there is

so little research and analysis available to guide the regulation of robo advisors

today and because the need for and corresponding returns to regulatory

oversight will increase as the scale of robo advice increases, we propose a

regulatory trajectory, rather than a regulatory agenda, that starts by building

the necessary human capital. Only then will policymakers be equipped to set

a regulatory agenda.

A. C

OMPONENTS OF ROBO ADVISORS THAT POSE REGULATORY

HALLENGES

In discussing these core components of robo advisors, our goal is to

provide a basic introduction and some examples of the issues that can arise in

assessing competence, honesty, and suitability. To make these assessments,

regulatory agencies will need to develop the appropriate, domain-specific

scientific and engineering expertise to go beyond our generalizations. Until

they do so, robo advisors will be “regulated” only by their contracting partners

and through the application of more general legal requirements such as those

71. See infra Part III.

72. See Stein, supra note 16 (“Clearly, if we want our markets to remain at the top, we need

to embrace innovation. And FinTech clearly promises some exciting advances. But, we also need

to be prepared to anticipate and ideally prevent problems before they arise. Remaining

competitive requires both market participants and regulators to thoughtfully evolve with

innovation, not react to it way after the fact.”).

73. See B

LACKROCK, supra note 22, at 8.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

734 IOWA LAW REVIEW [Vol. 103:713

governing privacy, data security, and unfair and deceptive trade practices.

While those existing safeguards and requirements provide some protection

for consumers, they are unlikely to be enough once robo advisors reach mass

market scale. Even if market forces and existing safeguards somehow manage

to ensure that robo advisors are honest and competent from the perspective

of individual consumers, they cannot address the problems of scale. As we

discuss in greater detail below, the problems of scale are classic collective-

action problems, in which the combination of individually rational actions

produces a collectively irrational result.

1. Ranking or Matching Algorithms and Processes

An algorithm is a formula, or formal statement of rules, that guides a

process.

For robo advisors, the key algorithms are those that rank the

financial products for consumers to select, and if the robo advisor makes the

selection, the algorithm then matches consumers with products. Each

algorithm is embodied in software code that is based on a model of how to

optimize the fit between the attributes of the financial products available to

the consumer and the attributes of the consumers who are using the robo

advisor.

Traditionally, analysts and developers that create matching and ranking

algorithms have an explicit, articulable model, based on ideas about which

product attributes are desirable to people with certain attributes. For

example, developers may program an investment robo advisor to recommend

that the mix of bond and stock funds in a consumer’s retirement savings

portfolio gradually shift over time so that the portfolio becomes more heavily

weighted with bond funds as the consumer ages.

Likewise, an analyst could

program a mortgage robo advisor to present the mortgages to a consumer in

a ranking that is negatively correlated with the estimated risk of payment

74. See generally 45 C.F.R. § 164 (2016) (setting forth the HIPAA regulations); REVISED UNIF.

DECEPTIVE TRADE PRACTICES ACT (NAT’L CONFERENCE OF COMM’RS ON UNIF. STATE LAWS 1966)

(providing the standard for some states’ laws regulating deceptive practices); Press Release, Dep’t

of Fin. Servs., Governor Cuomo Announces First-in-the-Nation Cybersecurity Regulation

Protecting Consumers and Financial Institutions from Cyber-Attacks to Take Effect March 1 (Feb.

16, 2017), http://www.dfs.ny.gov/about/press/pr1702161.htm (describing Governor Cuomo’s

cybersecurity regulation).

75. Garrett Hardin, The Tragedy of the Commons, 162 S

CI. 1243, 1244–45 (1968).

76. See Kroll et al., supra note 4, at 640 n.14; see also Mike Ananny, Toward an Ethics of

Algorithms: Convening, Observation, Probability, and Timeliness, 41 S

CI., TECH., & HUM. VALUES 93,

97 (2016) (“Computer science defines an algorithm as a ‘description of the method by which a

task is to be accomplished.’”).

77. This is standard procedure in a target date retirement fund. See, e.g., Olivia S. Mitchell

& Stephen Utkus, Target-Date Funds in 401(K) Retirement Plans 1–2 (Nat’l Bureau of Econ.

Research, Working Paper No. 17911, 2012).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 735

shocks or other features that increase the rate of default for a mortgage.

Further, developers may program a health insurance robo advisor to rank

health plans as a function of the total cost of the health plans for the

individual consumer or a function of a set of cost and quality factors.

Our sense is that most or all robo advisors presently in operation

primarily use algorithms of this sort, meaning that they adhere to a readily

explainable and examinable logic.

To assess the competence of these

algorithms, some of the information that regulators could require from the

consumer product intermediaries include:

(1) explanations of the models and the data upon which the models

are based;

(2) evidence regarding the appropriateness of the data used to

create the model, including the kinds of data-related problems

described in relation to customer and product data below;

(3) explanations of the outcomes that the algorithms are seeking;

(4) evidence that the algorithms perform in the way that they are

designed;

(5) evidence of how the creators of the robo advisor are measuring

whether the algorithm is succeeding and what they are doing in

response;

78. See, e.g., McCoy, supra note 54, at 153 (arguing in favor of disclosing the potential for

payment shocks and prepayment penalties).

79. See, e.g., Dellaert, Baker & Johnson, supra note 68, at 14 (reporting how consumer

selections changed when a leading private health insurance exchange shifted from a pure cost-

based ranking to a cost/quality based ranking); Ellen McGeoch et al., What’s the Bottom Line?

Total Cost Estimators on the Health Insurance Marketplace (June 26, 2017) (unpublished research

poster, AcademyHealth Annual Research Meeting) (reporting on variations in total cost calculators

among different health insurance exchanges). The Aon Retiree Exchange is an example of a

sophisticated health insurance robo advisor that considers both cost and quality in the U.S. context,

ON RETIREE HEALTH EXCHANGE, supra note 45.

80. In many other areas, and even for some aspects of some robo advisors today, software

developers use “machine learning” algorithms, which are quite different. Machine learning

algorithms are created by software programs that search for patterns in “big data.” Algorithms

created by machine learning programs identify predictive, not causal, relationships between

variables, and they are often not intelligible to humans, including their creators. Thus they

present a greater challenge to transparency than the kinds of algorithms we describe in the main

text. See generally Cary Coglianese & David Lehr, Regulating by Robot: Administrative Decision Making

in the Machine-Learning Era,

105 GEO. L.J. 1147 (2017) (exploring whether administrative law

restricts government agencies from using machine learning algorithms). For an example of a

health insurance robo advisor using machine learning techniques to build a component of the

model, see P

ICWELL, http://picwell.com (last visited Oct. 29, 2017) (explaining that Picwell uses

health insurance claims data and machine learning techniques to identify the “people like me”

aspect of the Picwell cost prediction algorithm).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

736 IOWA LAW REVIEW [Vol. 103:713

(6) explanations of what other alternatives the robo advisor creators

considered and rejected; and

(7) other kinds of evidence and explanations that people with more

specific domain knowledge can suggest.

Of course, gathering all this information is just a start. The regulator must

exercise good judgment based on this evidence, informed by domain-specific

expertise.

To assess the honesty of the algorithms, regulators will need to review the

accuracy of the algorithm descriptions provided to consumers as compared

to the information about the algorithms provided to the regulators.

Additionally, regulators will need to require demonstrations that the

algorithms do not take into account—directly or indirectly—factors that

would bias the outcomes in a way that harms consumers. For example, it

would be improper for a matching algorithm to take into account either the

size of the commission or fee paid to the financial product intermediary or a

proxy for that commission or fee.

Indeed, the fact that it is possible to

prevent algorithms from taking such factors into account represents a

significant improvement over a human-based system, as it has been

notoriously difficult to police the practice of steering clients and customers to

the products that provide the best benefits to the intermediaries, not to the

customers.

In the securities context, the 2016 FINRA report on digital investment

advice provides more detailed, domain specific descriptions of “effective

practices” for FINRA-registered firms to use to assess the algorithms in

investment robo advisors.

These practices could provide the basis for the

development of disclosure requirements, and it is likely that parties who

81. See, e.g., FINRA, supra note 1, at 5. We have been unable to find any examples of banking

or insurance regulators producing similar reports or guidance.

82. A recent lawsuit alleges that a robo advisor designer and a major investment management

company colluded “to design a robo-adviser program to steer [users] toward investments that paid [the

investment management company] high fees.” Diana Novak Jones, Morningstar, Prudential Face Class

Action over Robo-Adviser, L

AW360 (Aug. 4, 2017, 2:55 PM), https://www.law360.com/articles/951428/

morningstar-prudential-face-class-action-over-robo-adviser; see also U.S.

SEC. & EXCH. COMM’N, supra

note 17, at 3 (noting that an investment robo adviser, as defined in that guidance statement, is a

fiduciary and, thus, must provide disclosures that are “sufficiently specific so that a client is able to

understand the investment adviser’s business practices and conflicts of interest”). We question whether

mere disclosure should be adequate.

83. In our experience studying and working in and around the financial services industry

for decades, we have yet to meet anyone in the industry who believes that human consumer

product intermediaries compensated on commission are consistently able to resist steering their

customers to products that pay higher commissions. For research on this topic, see C

OUNCIL OF

ECON. ADVISERS, supra note 43; Schwarcz & Siegelman, supra note 40, at 36–37; Mullainathan et

al., supra note 51, at 1–5.

84. See FINRA, supra note 1, at 3–6. It is interesting to observe the degree of variation in the

investment algorithms employed by the (anonymous) firms that FINRA used as the basis for the report.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 737

believe that they have been injured by robo advisors will attempt to use these

practices as standards of conduct in the litigation context. This is the kind of

early effort that would be appropriate for the National Association of

Insurance Commissioners, or one of the active state insurance departments,

to take in the context of the emerging insurance web brokers that employ

simple recommendation tools and that the Consumer Financial Protection

Bureau should consider taking in the context of the emerging online tools

for comparing mortgages and credit cards.

2. Customer and Product Data

A high-quality ranking or matching algorithm will guide consumers to

high-quality decisions only in combination with adequate data of two types:

(1) the relevant attributes of the products available to the consumer, which

must include an adequate representation of the variety of potentially suitable

products available in the market to provide meaningful choice; and (2) the

relevant attributes of the consumers for whom the algorithm is ranking or

matching the products. There are two distinct categories of problems related

to both of these data types: access and quality.

Data Access. With the notable exception of publicly traded securities

and

some types of health insurance,

there are no public repositories of the kind

of detailed financial product attribute data that a robo advisor needs in order

to function. Therefore, absent robustly enforced legal requirements

obligating product suppliers to provide such data to public repositories, robo

advisors’ only source of financial product data is from the product suppliers

or their agents. These suppliers may be reluctant to provide these data for any

number of reasons. A supplier may not maintain electronic records of their

products that include all of the attributes employed in the robo advisors’

algorithms; the supplier may update the price or other attributes of its

85. See, e.g., COVERHOUND, https://coverhound.com (last visited Oct. 29, 2017) (providing an

example of an insurance webbroker); M

AGNIFYMONEY, http://www.magnifymoney.com (last visited

Oct. 29, 2017) (providing an example of a consumer credit comparison tool); see also Memorandum

from the Ctr. for Consumer Info. & Ins. Oversight, Health Ins. Marketplace Guidance 11–12 (Nov. 8,

2016), https://www.cms.gov/CCIIO/Programs-and-Initiatives/Health-Insurance-Marketplaces/

Downloads/Role-of-ABs-in-Marketplace_Nov-2016_Final.pdf (illustrating how one regulator is

attempting to regulate simple insurance robo advisers by requiring them to show the prices for all plans

available in the marketplace).

86. The easy access to comprehensive, public securities data may be the reason that

investment robo advisors are more developed than other robos. It is interesting to note that the

FINRA report does not mention access to product data, perhaps because of the assumption that

member firms already have access to all of the relevant securities data. With regard to customer

data, FINRA emphasizes only that for at least some kinds of investment advice it is important to

obtain a full description of the customer’s portfolio. See FINRA, supra note 1, at 11.

87. Health Insurance Marketplace Public Use Files (Marketplace PUFs), C

TRS. FOR MEDICARE

MEDICAID SERVS., https://www.cms.gov/cciio/resources/data-resources/marketplace-puf.html

(last visited Oct. 29, 2017).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

738 IOWA LAW REVIEW [Vol. 103:713

products more frequently and dynamically than the robo advisor is able to

accommodate; a supplier may believe that its product data records contain

valuable proprietary pricing or underwriting information that cannot be

trusted with the robo advisor; a supplier may believe that its products will not

show well in comparison to other products; or a supplier may not wish to sell

their products through the channels served by the robo advisor. Undoubtedly,

there are other business reasons that make a supplier reluctant to provide

data. Even if suppliers are obligated to provide some information about their

products to regulators, the data may not be adequate for robo advisors

because the regulator may not make the data publicly available, the data may

not be in machine readable form, the data may not include all of the relevant

product attributes, or the data may not be publicly released in time for the

robo advisor to use it.

Customer data can of course be collected directly from customers while

providing the robo advice to consumers. However, that can be burdensome

for the customers, and they may not in fact possess, or have easy access to, the

data that the robo advisor needs.

Thus, the more efficient and accurate

approach in many cases would be to collect consumer data from third

parties,

but those third parties may not maintain the information in a format

that is accessible or they may not be willing to provide the information due to

concerns related to fraud, legal constraints on providing the data, or other

reasons.

Regulators should be asking three kinds of questions related to accessing

data. First, has the robo advisor obtained access to reasonable sources of data,

and are there any concerns that inability to obtain data, particularly regarding

products, will bias the rankings and matching in a way that disadvantages

consumers in relation to intermediaries and sellers? Second, where there are

gaps in data, what are the strategies that the robo advisor considered to

address those gaps, why did the robo advisor choose the strategy(ies) that it

employed, and was that choice reasonable? Third, does the regulator have the

88. For example, a health insurance robo advisor may need medical utilization records, a

mortgage robo advisor may need detailed income or expense records, and an investment robo

advisor may need detailed asset/investment records.

89. See PWC:

FS VIEWPOINT, SIGNIFICANT OTHERS: HOW FINANCIAL FIRMS CAN MANAGE THIRD

PARTY RISKS 4, 8 (2015), https://www.pwc.com/us/en/financial-services/publications/viewpoints/

assets/pwc-third-party-vendor-risk-management.pdf (showing that 57% of survey respondents have an

accurate inventory of all third parties that handle sensitive firm, employee, and customer data).

90. Vendor and Third Party Management, FFIEC: IT

EXAMINATION HANDBOOK INFOBASE, http://it

handbook.ffiec.gov/it-booklets/retail-payment-systems/retail-payment-systems-risk-management/

operational-risk/vendor-and-third-party-management.aspx (last visited Oct. 29, 2017) (providing

guidelines for third-party data risk management); PWC, supra note 90, at 1 (noting that third parties

have created problems for financial services institutions by mishandling consumer data).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 739

authority, whether formal or informal, to increase access to data and thereby

improve the quality of the robo advice?

Data quality. Even assuming the data are available, there will be significant

problems regarding the completeness and accuracy of the data, particularly

in the early stages of the development of robo advisors.

In our interactions

in and around the financial services field, we have found that there is an

inverse relationship between people’s experience working with data and their

expectations regarding the completeness and accuracy of data. In our

experience, people who work with data always expect to find problems with

data; people who do not work with data tend to over-estimate the

completeness and accuracy of data. To the extent that regulators lack

experience, they may be over-trusting robo advisor assurances and, thus,

demand less evidence that the robo advisors have adequately tested the

accuracy and completeness of the data and that they have developed

reasonable strategies to deal with missing or obviously incorrect data.

Accordingly, regulators need to develop the capacity to ask hard, domain-

specific questions about data quality and to evaluate the responses.

3. Choice Architecture

Robo advisors typically use automated processes to communicate their

advice, whether directly to consumers in the case of a consumer-facing robo

advisor or to a human intermediary in the case of more traditional automated

tools. In the case of a consumer-facing robo advisor, there may also be the

option, or even requirement, of closing the sale with human assistance

through a call center or a chat function.

In all these cases, the robo advice

is embodied in a ranked set of alternatives and information about those

alternatives. Behavioral science research demonstrates the very large effects

that choice architecture—the organization of the context in which people

make decisions—can have on decisions.

For example, the order in which

options are presented, the number of options that are presented, the

attributes of the options presented (and in which order), the framing of

91. The CFPB’s recent focus on encouraging banks to provide access to personal financial

management software services is encouraging, as those services are likely to be an important

provider of robo advice. See CFPB, Request for Information Regarding Consumer Access to

Financial Records, 81 Fed. Reg. 83,806, 83,808 (Nov. 22, 2016).

92. See, e.g., M

ASS. SEC. DIV., supra note 22, at 5 (noting that one of the problems of robo advisors

is data inaccuracy).

93. See, e.g.,

EHEALTH, https://www.ehealthinsurance.com (last visited Oct. 29, 2017) (providing

chat and call center options).

94. Thaler and Sunstein coined the term “choice architecture.” R

ICHARD H. THALER & CASS R.

SUNSTEIN, NUDGE: IMPROVING DECISIONS ABOUT HEALTH, WEALTH, AND HAPPINESS 3–4, 81–100

(2008). The literature is vast and growing. For a recent review, see generally Eric J. Johnson et al.,

Beyond Nudges: Tools of a Choice Architecture, 23 M

ARKETING LETTERS 487 (2012) (examining the tools

available for choice architects and their importance to decision-makers).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

740 IOWA LAW REVIEW [Vol. 103:713

options (e.g., gain versus loss), all have major impacts.

This means that the

way that robo advice is presented can have a profound effect on whether and

how consumers use that advice.

Thus, as in the case of ranking or matching

algorithms, there may be a role for regulators in assessing the competence

and honesty of the choice architecture and the suitability of the products that

it favors.

As Richard Thaler is fond of saying, salesmen have always intuitively

understood the power of choice architecture.

Systematic research on choice

architecture provides a vocabulary and a set of assessment tools. The

challenge for financial services regulators is to gather and extend the relevant

domain specific research. The goal should be developing the capacity to

evaluate whether robo advisors’ choice architecture is appropriate and does

not undermine the quality of the ranking or matching algorithms.

Incompetent choice architecture may lead consumers away from the best-

ranked products or prevent them from buying altogether. For example, this

could occur if robo advisors present products in an overly complex fashion.

Regulators should also be looking out for choice architecture techniques that

steer consumers in a manner that benefits the intermediary notwithstanding

a neutral, merits-based ranking or matching algorithm.

There is too much learning from the choice architecture research to

meaningfully summarize it here. What we can do is to note that behavioral

scientists are developing ideas about best practices that will be useful to both

robo advisors and regulators learning to assess robo advisors.

For example,

robo advisors could assist consumers by making it easy to access their

personally highest-ranked products in the market—even if those products are

not the most profitable for the intermediary to sell—or they could update

consumers on a yearly basis if it would be beneficial for them to switch to a

more fitting product. Robo advisors could also design ranked sets of products

in a format that facilitates easy consumer comparison and provides assistance

in making a decision.

Finally, rigorous experimental testing is an important

best practice that provides a record that could be made available for

regulators to review in assessing whether the robo advisors have engaged in a

meaningful and empirically informed choice architecture effort.

95. Johnson et al., supra note 94, at 488–96.

96. See, e.g., Johnson et al., supra note 7, at 4–8 (demonstrating that pre-checking the best

value plan and providing a simple demonstration of how the best value plan was selected

substantially increased the percentage of consumers that chose the best value plan as compared

to simply showing the consumers the value of the plans).

97. See E-mail from Richard Thaler, Professor, John R. Raben/Sullivan & Cromwell

Fellowship Lecture, Yale Law School, to author (Aug. 25, 2017, 3:18 PM) (on file with author).

98. See Memorandum from John P. Holdren, Dir., to Heads of Exec. Dep’ts & Agencies

(Sept. 15, 2016) (providing best practices guidance for agencies seeking to apply behavioral

science insights to federal programs).

99. See id. at 2, 6.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 741

Experimental testing, verification that the testing occurred, and

verification that designers implemented the choice architecture that came out

best for consumers in the testing, is easiest to do when the choice

environment is fully automated.

100

To our knowledge, there has not yet been

research on “hybrid robo” environments, such as (1) a consumer financial

product intermediary that uses robo advisors behind the scenes and humans

to interact with customers; or (2) a web-based intermediary that encourages

or requires customers to make a phone call or use the chat function to ask

questions and close the deal. Intuitively, the human/machine handoff

provides significant opportunities to take advantage of consumers, for

example through a “bait and switch,” which involves offering additional

options or pointing out features the robo advice did not emphasize. To

evaluate these risks, regulators could require the intermediary to track the

robo advice provided in each case and the products that the consumer

purchased so the regulator could evaluate whether there are any systematic

patterns to the cases in which the consumers did not follow the robo advice

and, if so, require the intermediary to explain those patterns and demonstrate

that the patterns are in the consumers’ best interests. Indeed, the

intermediary should already be collecting and analyzing that information for

its own purposes. Thus, requiring that information should not impose an

unreasonable burden.

Notably, the 2016 FINRA report regarding the identification of effective

practices regarding algorithms and customer data is silent on the topic of

choice architecture.

101

This is an unfortunate gap in an otherwise forward-

thinking report, especially due to the consumer exploitation risks in the

machine/human interface.

4. Information Technology Infrastructure

Assessing the security and stability of information technology

architecture is an increasingly important aspect of financial services

regulation that extends well beyond robo advisors.

102

Financial services

regulators already appear to recognize the need to enhance their capacities

in this area,

103

and the technical aspects of this lie far from our comparative

advantage. Accordingly, we will not address this topic further, other than to

offer two related observations. First, overly demanding information

technology infrastructure requirements could serve as barriers to entry for

100. John R. Hauser et al., Website Morphing, 28 MARKETING SCI. 202, 213 (2009).

101. See generally FINRA, supra note 1. Perhaps for this reason the September 2016 BlackRock

white paper on digital investment advice does not address choice architecture either. See generally

LACKROCK, supra note 22.

102. See, e.g., Press Release, Dep’t of Fin. Servs., supra note 32; see also B

LACKROCK, supra note

22, at 8.

103. See, e.g., Press Release, Dep’t of Fin. Servs., supra note 32.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

742 IOWA LAW REVIEW [Vol. 103:713

innovative new enterprises.

104

Second, regulators could address this concern

by developing a strategy for new market entrants that increases the level of

scrutiny with the scale of the enterprise and forgoing such scrutiny altogether

for early-stage robo advisors and those with small market share, provided the

small entrants only sell their services to businesses that have significant

incentive to ensure an adequate information technology infrastructure.

105

B. SCALE AND THE CONCEPT OF A REGULATORY TRAJECTORY

At a general level, the benefits of closer regulation of consumer financial

product advice depend on the amount of harm that the advice could cause to

consumers. Conceptually, the amount of this harm is a function of three

factors: (1) the number of consumers affected by the potentially harmful

action; (2) the probability of the harmful action occurring in the market, and

(3) the severity of the consequence of the harmful action to the consumer.

When more consumers are affected, when the harmful action is easy to

introduce into the market, and when the severity of harm from the action is

high, the need for regulation is greater.

All three factors have the potential to increase along with the market

share of robo advisors. First, a successful robo advisor has the capacity to reach

many more consumers than any single human advisor. Second, as the market

share of robo advisors increases, there will be greater opportunities for robo

advisors to fail. Third, because robo advisors may give more comprehensive

and detailed advice than any single human advisor,

106

the potential harmful

consequences of the robo advice to the individual consumer may be larger

than that of a human advisor who operates within a narrower domain. Fourth,

if one robo advisor gains truly massive market share, or if the models

underlying competing robo advisors are sufficiently alike, there is a risk of

104. KPMG recently identified “[c]ybersecurity and consumer data privacy” as one of the “[t]en

key regulatory challenges [f]acing the financial services industry in 2017.” P

HILIP MACFARLANE

KAREN STAINES, KPMG, TEN KEY REGULATORY CHALLENGES FACING THE FINANCIAL SERVICES

INDUSTRY IN 2017 1, 5 (2017), https://assets.kpmg.com/content/dam/kpmg/sg/pdf/2017/02/Ten-

key-regulatory-challenges-facing-the-financial-services-industry.pdf. This report identified a range of

regulatory activity taking place across multiple agencies and levels of government. Id. at 1. Just keeping

track of all of the potentially applicable changes could be a difficult challenge for a small company. See

id. (laying out potential financial regulatory reforms which may adversely affect businesses).

105. For examples of regulators attempting to be more accessible and flexible for emerging

businesses, see generally C

ONSUMER FIN. PROT. BUREAU, PROJECT CATALYST REPORT: PROMOTING

CONSUMER-FRIENDLY INNOVATION (2016), https://www.consumerfinance.gov/documents/1331/10

2016_cfpb_Project_Catalyst_Report.pdf; F

IN. CONDUCT AUTHORITY, REGULATORY SANDBOX (2015),

https://www.fca.org.uk/firms/project-innovate-innovation-hub/regulatory-sandbox.

106. For example, a robo advisor may be able to provide recommendations based on more

advanced personalized projections of future income streams or spending patterns and

considering a broader portfolio of financial products than a human advisor.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 743

highly correlated losses that could even pose systemic risk.

107

Finally, as robo

advisors gain scale, there may be collective-action problems that arise from

ranking and matching services that are individually rational but have perverse

consequences for financial product markets.

To further illustrate this line of reasoning, consider the impact of Google

or Yelp on tourists’ search for a restaurant in a new town as compared to the

traditional approach of asking the hotel concierge for a restaurant

recommendation. Google’s scale compared to the concierge is tremendous.

It provides access to restaurant information to all tourists in all towns, and it

is easily accessible to everyone. If it gives systematically bad restaurant advice,

the impact will be much greater than bad advice given by any individual

concierge. Even if the advice given by hotel concierges is on average just as

bad, the advice given by many individual concierges would be bad in many

different ways. Of course, the consequences of providing poor restaurant

advice even on a large scale seem sufficiently small that regulating Google’s

or Yelp’s restaurant reviews seems unlikely to be necessary. However, the

consequences of poor financial advice can be severe even in an individual

instance, and potentially catastrophic on a large scale.

The potential collective-action problems are more difficult to predict.

One potential example relates to a familiar problem in machine learning: the

trade-off between exploitation and exploration in learning algorithms.

108

an algorithm is to learn, it must sometimes make a choice that is less than

optimal, based on current information.

109

It must explore, rather than exploit.

Yet, when the algorithm is part of a robo advisor, each individual consumer

would prefer that the algorithm exploit, just as each individual user of the

navigation app Waze

would prefer that the app provide the shortest route

based on known information and not send the user on an exploratory

route.

110

The second potential collective action problem results from the game

theoretic nature of driving. Continuing with the Waze example, my travel time

is a function not only of the distance between point A and point B but also of

who else is on the road, and I can only control my own driving. If Waze

employs an individually rational, or greedy, algorithm, it will always give me

the best route in light of what everyone else is doing. Yet, as the famous

107. See O’NEIL, supra note 5, at 199–218; cf. CHARLES PERROW, NORMAL ACCIDENTS: LIVING

WITH

HIGH-RISK TECHNOLOGIES 3–14 (Princeton Univ. Press 1999) (1984).

108. See generally John Langford et al., Competitive Analysis of the Explore/Exploit Tradeoff

(2002), https://www.cs.cmu.edu/~jcl/papers/exp_exp_icml/icml_final.pdf (investigating the

explore/exploit trade-off in reinforcement learning using learning algorithms).

109. Id. at 1–2.

110. See W

AZE, https://www.waze.com/en-GB (last visited Oct. 29, 2017) (describing an app

which provides live traffic information and routes drivers to their destinations using the shortest

route possible taking into consideration traffic patterns and trends).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

744 IOWA LAW REVIEW [Vol. 103:713

Braess’s paradox shows, individually rational driving behavior can lead to

slower traffic overall, as everyone competes for the best route.

111

Thus,

widespread adoption of a greedy Waze program could lead to longer drives

for everyone. There are solutions, but those solutions require marginal cost

pricing, which Waze cannot implement on its own, or for Waze to send some

drivers on slower routes than other drivers, which violates the assumption of

individually greedy route assignment.

112

Algorithms that favor some people

over others should of course raise red flags.

Financial services also have a game theoretic structure. The cost of my

insurance is a function not only of my risk but also of the risk of the other

people in my pool. Similarly, the cost of my credit is a function not only of my

credit risk but also the credit risk of the other people who are in my pool. In

both cases, my costs depend on who is in the pool because the pool bears the

costs of the insurance claims and bad debts of the members of the pool. In a

world of perfect information, risk-based pricing could cut the link between

my cost and those of the pool, but we do not live in that world.

113

Finally, the

returns on my portfolio depend not only on the underlying businesses whose

shares and bonds are in the portfolio, but also the investing behavior of other

people.

114

At sufficient scale, robo advice can shape insurance and credit

pools and even move investment markets. For example, the tsunami of index

investing that is currently reshaping the mutual fund industry is the result of

a distributed kind of robo advice in which algorithms supplant individual fund

managers.

115

There is surely much more to come.

At this point, all such risk assessments can only be conceptual and

speculative. Since robo advice is still not widely adopted in the market, the

current lack of broad-based regulatory capacity seems unlikely to have done

111. Braess’s paradox demonstrates mathematically how the introduction of a new, faster link

between two points can paradoxically reduce the mean driving time, despite the introduction of

new road capacity. See Eric I. Pas & Shari L. Principio, Braess’ Paradox: Some New Insights, 31 T

RANSP.

RES. PART B: METHODOLOGICAL 265 (1997) (reviewing literature regarding the paradox).

112. See generally id. (explaining the circumstances under which the paradox holds and how

marginal cost pricing can solve the problem).

113. Cf. Tom Baker, Containing the Promise of Insurance: Adverse Selection and Risk Classification,

9 C

ONN. INS. L.J. 371 (2003) (describing how insurance companies classify policy holders into

risk pools because insurance companies lack complete information about policy holders’

behaviors and risk aversion).

114. See generally Lawrence Harris & Eitan Gurel, Price and Volume Effects Associated with Changes

in the S&P 500 List: New Evidence for the Existence of Price Pressures, 41 J.

FIN. 815 (1986)

(demonstrating that the price of a stock rises when it is added to the S&P 500).

115. See Tom Petruno, Small Investors’ Move to ‘Passive’ Stock Funds Becomes a Stampede, L.A.

TIMES (Apr. 9, 2017, 3:00 AM), http://www.latimes.com/business/la-fi-investing-quarterly-

index-funds-20170409-story.html.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 745

much harm—yet.

116

The time has come for the financial services authorities

to develop the capacity to engage in a more systematic risk assessment.

In that effort, it makes most sense to think of financial services regulation

as following a dynamic regulatory trajectory. The first step is gathering

information to assess what capacities the agencies need to develop, and some

regulators have begun to do just that.

117

One useful model for this step is the

market review commonly undertaken by the Financial Conduct Authority

(“FCA”) in the U.K. as an early step in their regulatory process.

118

Notably, the

FCA conducted a broad Financial Advice Market Review that produced a final

report in March 2016 that addressed automated financial advice as a potential

solution to the problem of a lack of broad access to financial advice.

119

While

that report did not address the kinds of regulatory challenges this Essay has

identified, automated advice was not the central focus of the review. Now that

automated financial advice has received such positive attention, the next

logical step for the FCA and other regulators is to consider the challenges

involved in ensuring that automated advice lives up to its potential.

After the market review and associated assessment of regulatory capacity,

the next step is to develop the necessary regulatory capacities. In this phase,

regulatory authorities will not be starting from ground zero, as the large

financial services organizations that are purchasing robo advice services are

already developing methods for assessing the quality of those services.

120

Thus,

the regulatory agencies will simply need to address a “make or buy” decision

about the necessary expertise.

After developing the appropriate regulatory capacities, regulators will be

equipped to formulate a strategy that addresses the challenges involved in

adapting to the scale and consequences of robo advice in the market in a

manner that promotes both effective innovation and honest and competent

robo advising. Over time, financial services regulators will likely take an

increasingly strict approach toward safeguarding the competence and honesty

of robo advisors and the suitability of their advice, but much of this oversight

can be accomplished on an automated basis and, as discussed below, it will

116. Note that, although we are not proposing application of the precautionary principle

here (because the small market share of robo advisors reduces the concerns), we expect that

other commentators may do so. Cf. Cass R. Sunstein, Beyond the Precautionary Principle, 151

U. PA.

L. REV. 1003, 1003 (2003) (noting the strength of the precautionary principle “in legal systems

all over the world”).

117. See, e.g., FINRA, supra note 1; U.S.

SEC. & EXCH. COMM’N, supra note 17; Press Release, N.Y.

State Dep’t of Fin. Servs., supra note 6.

118. See generally F

IN. CONDUCT AUTH., FINANCIAL ADVICE MARKET REVIEW: FINAL REPORT

(2016) (describing the market review for the year 2016).

119. Id. at 28.

120. See FINRA, supra note 1, at 13 (identifying best practices that assume the availability of

services to evaluate the competence and honesty of digital investment advisors).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

746 IOWA LAW REVIEW [Vol. 103:713

reflect and encourage the current dynamism in the broader financial

technology market.

Of course, these are broad generalizations. However, that is all that is

possible before the financial services authorities engage in the kind of

systematic, interdisciplinary assessment that we advocate in this Essay.

IV. C

ONCLUSION: BEYOND BASIC HONESTY, COMPETENCE, AND SUITABILITY

The designers of robo advisor technology and the regulators of robo

advisor technology have a ways to go before robo advisors reach their potential

and deliver high-quality advice to a mass-consumer market across a broad

range of financial services.

121

Nevertheless, it is not too early to begin thinking

about what comes next. For that purpose, it is useful to assume that regulators

will develop the capacity to confirm that robo advisors do what their creators

and operators say, based on access to data of adequate quality, and that

regulators will gain the authority to remove from the market robo advisors

who cannot or will not prove their capabilities to regulators.

122

That means

that, in the near future, consumers will have access to well-designed robo

advisors that honestly and competently recommend suitable financial

products for consumers across the entire spectrum of financial products,

employing appropriate choice architecture and reliable information

technology infrastructure.

If we assume this basic competence and honesty for the moment, we can

look ahead to other challenges and opportunities. In this concluding Part, we

present one such challenge and two opportunities. The challenge is fostering

a market in which an evolving diversity of robo advisors and consumer

financial product intermediaries compete based on the measurable quality of

their advice and related services for consumers. The opportunities are a leap

forward in the ability to hold consumer financial product intermediaries

accountable and a new approach to consumer financial product regulation

that fosters more diversity in the forms and features of consumer financial

products to better match the heterogeneity of consumers.

The challenge. While some regulatory oversight of the core components of

robo advisors seems necessary to ensure basic competence and honesty, there

is a risk that regulatory oversight will be watered down in the face of pressure

121. See BLACKROCK, supra note 22, at 1 (“While digital advisors represent a very small

segment relative to more traditional financial advice providers, their recent rapid growth suggests

a need for a focused analysis of the business and activities of these advisors.”).

122. See, e.g., M

ASS. SEC. DIV., POLICY STATEMENT: STATE-REGISTERED INVESTMENT ADVISERS’ USE

THIRD-PARTY ROBO-ADVISERS 1, https://www.sec.state.ma.us/sct/sctpdf/Policy-Statement-State-

Registered-Investment-Advisers-Use-of-Third-Party-Robo-Advisers.pdf; M

ASS. SEC. DIV., supra note 22,

4–6; T

HE BD. OF THE INT’L ORG. OF SEC. COMM’NS, REPORT ON THE IOSCO SOCIAL MEDIA AND

AUTOMATION OF ADVICE TOOLS SURVEYS 21–22 (2014), http://www.iosco.org/library/pubdocs/pdf/

IOSCOPD445.pdf.

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 747

by powerful financial services enterprises that have thrived in the past.

123

addition, regulatory oversight poses the risk of discouraging innovation by

serving as a barrier to entry into the market for robo advisors. Moreover, as

regulators develop preferences about robo advisor design, and as regulated

entities come to understand those preferences, oversight may lead to a model

convergence that increases the risk of catastrophic failure.

124

To counter these risks, we propose that, in addition to their oversight

activities, regulatory agencies also develop contests of contests, in which the

agencies provide substantial cash prizes to organizations that develop contests

in which robo advisors compete based on measurable differences in the

quality of their components—especially the ranking and matching

algorithms, choice architecture, and data access and efficiency—as well as

their overall performance. A contest of contests promotes diverse, innovative

ways to measure success. That diversity and innovation in measuring success

will promote diversity among and innovation by robo advisors, and because

the contests will themselves change over time, there will be less risk that all

robo advisors will be tuned to any single way of measuring success.

The accountability opportunity. The automated nature of robo advisors

means that robo advice inputs and outputs, algorithms, and much of the

choice architecture exists in digital form, and, thus, can be analyzed using

digital tools. Indeed, it should be possible to store the customer and product

inputs, advice algorithm, choice interface, market conditions, and outcome

for every individual customer interaction and to link all the interactions of the

firm with that customer over time, creating the robo advisor analog to the

“black boxes” proposed for self-driving cars.

125

Whether financial services regulators presently have the legal authority

to require firms to retain these data and make them available for analysis is a

domain-specific legal question that lies beyond the scope of our analysis.

126

123. See BARR ET AL., supra note 13, at 644–48 (noting objections to adopting the fiduciary

standard for all investment advisors, including traditional stock brokers, on the grounds that

trying to apply that standard to stock brokers will water down the standard rather than improve

consumer protection).

124. See F

IN. CONDUCT AUTH., supra note 118, at 26 (noting the concern that robo advisors

may stop innovating if the government becomes too actively involved). The FCA did not make

the point about catastrophic risk. See generally id.

125. See Darrell Etherington, Germany Wants a ‘Black-Box’ in Any Car with Self-Driving Features,

ECHCRUNCH (July 18, 2016), https://techcrunch.com/2016/07/18/germany-wants-a-black-

box-in-any-car-with-self-driving-features.

126. The transparency complications raised by the use of machine learning algorithms is also

outside the scope of our analysis for two reasons. First, our understanding is that machine

learning algorithms are not presently used by robo advisors to any significant extent. Moreover,

the issues are presently under close investigation in relation to the government’s use of machine

learning algorithms. See, e.g., Coglianese & Lehr, supra note 80, at 1147 (proposing that, although

currently under close government watch, the use of machine learning ought to “pass muster

under core, time-honored doctrines of administrative and constitutional law”). Financial services

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

748 IOWA LAW REVIEW [Vol. 103:713

The main point is that these data could lead to a leap forward in our ability to

hold consumer product intermediaries accountable for providing misleading,

incomplete or otherwise inadequate advice. Similar to the black boxes in self-

driving cars, these data would provide a record for analysis if the consumer

has an “accident”—in this context, meaning that the consumer suffers a

financial misfortune related to a consumer financial product—making it

possible to evaluate the role of the intermediary and to determine whether to

hold the intermediary accountable.

The diversity opportunity. From a consumer protection standpoint, the

history of financial services innovation has not been kind to advocates of

complexity and choice. Across all three of the major financial services sectors,

consumer product intermediaries have used complexity and choice to take

advantage of consumers, particularly those who are less sophisticated.

127

The

days are gone in which policymakers believe that a minority of well-informed

and careful shoppers—Thaler and Sunstein’s “Econs”—can make a market

fair when that market is structured to exploit fallible humans.

128

In response,

consumer protection advocates have called for a return to “plain vanilla”

financial products.

129

This is not because they believe that vanilla is best for

everyone, but rather because the evidence shows that choice and complexity

regulators will be able to “piggy back” on the results of those investigations if and when machine

learning algorithms become important in the robo advisor context.

127. See, e.g., Tara Siegel Bernard, Even Math Teachers Are at a Loss to Understand Annuities, N.Y.

TIMES (Oct. 28, 2016), https://www.nytimes.com/2016/10/29/your-money/403b-teachers-

annuities.html; Tara Siegel Bernard, Think Your Retirement Plan Is Bad? Talk to a Teacher, N.Y.

TIMES (Oct.

21, 2016), https://www.nytimes.com/2016/10/23/your-money/403-b-retirement-plans-fees-

teachers.html; Tara Siegel Bernard, An Annuity for the Teacher—and the Broker, N.Y.

TIMES (Oct. 26,

2016), https://www.nytimes.com/2016/10/27/your-money/403-b-retirement-plans-teachers-

brokers-fees.html; Jackson & Burlingame, supra note 55, at 296, 308.

128. See, e.g., Tom Baker & Peter Siegelman, “You Want Insurance with That?” Using Behavioral

Economics to Protect Consumers from Add-On Insurance Products, 20 C

ONN. INS. L.J. 1, 37 (2013)

(reviewing prior economic literature and summarizing behavioral economics research

demonstrating that a small number of informed shoppers cannot prevent sellers from taking

advantage of others).

129. See, e.g., M

ICHAEL S. BARR ET AL., NEW AM. FOUND., BEHAVIORALLY INFORMED FINANCIAL

SERVICES REGULATION 9–10 (2008), http://repository.law.umich.edu/cgi/viewcontent.cgi?article=10

28&context=other (suggesting that a plain vanilla set of default mortgages would be easier to compare

across mortgage offers); Elizabeth Warren, Three Myths About the Consumer Financial Product Agency,

ASELINE SCENARIO (July 21, 2009), http://baselinescenario.com/2009/07/21/three-myths-about-

the-consumer-financial-product-agency (noting that the promotion of “plain vanilla” contracts is

CFPA’s real regulatory break-through).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:38 PM

2018] REGULATING ROBO ADVICE 749

lead to exploitation and regressive cross subsidies.

130

They believe, with some

reason, that vanilla is good enough for most people.

131

Once consumers have—and use—easy access to robo advisors, the

analysis could change. A good robo advisor gives an unsophisticated

consumer more processing power than even the most sophisticated consumer

working on her own. That could lead to a fundamental shift in regulatory

strategy: from regulating the content of consumer financial products to

(1) facilitating access to the data needed to make robo advisors work; and

(2) taking appropriate measures to verify the quality of the robo advisors and

the public access to them. This is a disclosure-based regulatory strategy with a

twist: electronic disclosure of product attributes to robo advisors; an easy

procedure for consumers to authorize electronic disclosure to robo advisors

of their own relevant financial or other relevant data; and robo advisor

disclosure of the data needed to verify their competence and honesty to the

appropriate regulatory authority.

* * *

Our goal in this Essay has been to open a discussion within legal and

financial services scholarship that invites the participation of those with

expertise in other relevant disciplines. In keeping with this goal, we have

raised more questions than we have answered, and we have barely sketched

the outlines of even the best of our ideas. However, that is appropriate at this

stage of the regulatory trajectory.

As robo advisors grow in scale, protecting the integrity of financial

markets will require the kind of cross-disciplinary cooperation that regularly

occurs in the domains of health and environmental regulation. As we

observed at the outset, people design, implement and market robo advisers,

and it cannot simply be assumed that they can or will always act in consumers’

best interests. The lawyers, economists, and behavioral scientists already

involved in financial services regulation will need to understand enough

about computer and data science to craft and apply new regulatory strategies,

and the computer and data scientists at the forefront of the innovation will

need to understand enough about legal structures and ways of thinking to

help make the new regulatory strategies sensible. The benefits from these

efforts almost certainly will exceed the costs, because the very same returns to

scale that make robo advisors so cost-effective lead to similar returns to scale

in assessing their quality. Coordinating that effort is a logical and important

130. MICHAEL S. BARR ET AL., JOINT CTR. FOR HOUS. STUDIES, HARVARD UNIV., BEHAVIORALLY

INFORMED HOME MORTGAGE CREDIT REGULATION 19–20 (2008), http://www.jchs.harvard.edu/

sites/jchs.harvard.edu/files/ucc08-12_barr_mullainathan_shafir.pdf (noting that the market in some

cases would like to exploit or exaggerate consumer fallibility).

131. Warren, supra note 129 (noting that “plain vanilla” contracts would meet the needs of

about 95% of customers).

BAKERDELLAERT_PP_FINAL (DO NOT DELETE) 1/7/2018 11:37 PM

750 IOWA LAW REVIEW [Vol. 103:713

role for our expert financial services regulators. It is time for them to develop

the necessary expertise.