2014 Poster Sessions : Insurance in the World of Big Data

Student Name : Eric Huang
Advisor : Yoav Shoham
Research Areas: Artificial Intelligence
In the Information Age today, new business models in the form of exchanging personal information for free services have become increasingly prevalent, and an increasing amount of personal data is being collected, directly or indirectly. In this paper, we explore the implications of this increasing availability of personal information in the context of insurance markets. We find via computational experiments that in a monopolistic market, people are worse off as the insurance company gains free access to more personal information since they can discriminate people more accurately. We explore variants of our model including letting people charge for their information, a competitive insurance market, and inaccurate use of data. By introducing costly information, both the insurance company and the population as a whole can benefit from making information available. In a competitive market, more public information is better for the prospective buyers as a whole.

However, the common theme is that it always comes at the expense of making the higher-risk group worse off.

Finally, we explore the importance of accurate data analysis tools for the insurance company, and how the prospective buyers could potentially game the system to their benefit if the data analysis is inaccurate.

Eric Huang is a PhD student in Computer Science at Stanford University, where he is advised by Professor Yoav Shoham. Eric's research interests lie in the intersection of computer science and economics, particularly in the economics of information. He is fortunate to be supported by a Stanford Graduate Fellowship.

Eric is very lucky to be mentored by Jaron Lanier and W. Brian Arthur during a summer internship at Microsoft Research in 2013. Before coming to Stanford, Eric graduated from Harvard University in 2010 with a B.A. in Applied Mathematics/Computer Science and a minor in Economics.