1. 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

7. 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?

8. 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

9. 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

10. 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