Regulating Robo Advice Across the Financial Services Industry

• Author: Tom Baker & Benedict Dellaert
• Position: William Maul Measey Professor at the University of Pennsylvania Law School/Professor, Department of Business Economics, Marketing Section, School of Economics, Erasmus University Rotterdam
• Pages: 713-750

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.

I.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.

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.1 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 prom ote the development of automated finan cial advice to

increase access to financial advice); infra Part III (discussing the cost-effect ive 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/

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.2 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. 3 In addition, there are

concerns that automation may entrench historical unfairness4 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.5

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,6 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 AM. 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, WALL 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., WENDELL 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 CALIF. 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.h tml

(“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 rely ing 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 sy stems). For an effort by the tech industry to add ress some of these

challenges, see PARTNERSHIP 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 CONN. 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 Cyberse curity