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

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. P OLICY J USTIFICATIONS AND R EGULATORY O BJECTIVES ............. 721 B. R OBO A DVISORS : C OMPETENCE , H ONESTY , AND S UITABILITY .... 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. C OMPONENTS OF R OBO A DVISORS THAT P OSE R EGULATORY C HALLENGES .......................................................................... 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. S CALE AND THE C ONCEPT OF A R EGULATORY T RAJECTORY ........ 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 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. FIN. 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 unfairness 4 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-topay-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.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 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 Cybersecurity 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...