What is a robo advisor? An automated service that ranks, or matches consumers to, financial products on a personalized basis, embedded in: Consumer financial product intermediaries: Insurance exchanges, brokers & companies: Financial advisors & broker dealers Mortgage brokers Lead generation services: Domain specific media companies: Zillow Web based financial advice/mgt services: Mint.com, NerdWallet Web-based advertising aggregators Goal is to identify RA as a category across financial services sector presenting similar challenges & opportunities in order to get sectors thinking cooperatively and comparatively. RA as generic category not regulated anywhere in financial services sector Regulated entities that use them are, by definition, regulated; but supervision not targeted at RA’s, with exception of some best practices guidance by FINRA Lead generation services are completely unregulated, except through whatever rules govern advertising more generally

Research questions: What is the proper role of gov’t in: Monitoring the quality of robo advice (as a 21st century analog to regulation of human intermediaries’ competence and honesty)? Making data available to facilitate robo advice? If people had access to, and took advantage of, decent quality financial product robo advice, what could be the consequences for financial services regulation?

Where I am now: Understanding how robo advisors work Making the case that insurance/banking/securities robo advisors are sufficiently similar that they should be looked at as a group Identifying what a regulator that wants to do quality control (or make an ex post assessment) needs to know first Evangelizing for regulators across sectors and jurisdictions to talk to and learn from each other to improve ideas about whether/when/how to “regulate” robo advisors Developing ideas about what comes after setting minimum competence and honesty standards for robo advice Looking for points of connection with computer science & data science for reseach projects

Regulatory focal points Algorithms/models: Data: Financial products & attributes Consumer attributes Choice architecture (user interface) IT infrastructure Core component Bias, competence, fairness Access, quality, Producers won’t provide Consumer data incomplete, biased Bias, competence Security, privacy, reliability Regulatory concern

Beyond basic competence Challenge: Promoting diversity and quality among robo advisors Contests (and contests of contests) Opportunities: Greater intermediary accountability Greater product diversity without the paradox of choice