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[Colloquium] Risk Scoring and Future Directions

February 2, 2012

Watch Colloquium: 

M4V file (700 MB)

  • Date: Thursday, February 2, 2012 
  • Time: 11:00 am — 12:15 pm 
  • Place: Mechanical Engineering 218

Joseph R. Barr
Chief Scientist at ID Analytics

Part 1: ID Analytics main business is scoring applications (for credit/services) for risks including identity/ authenticity & credit. By definition an application is a vector of identity elements (SSN, Name, Address, Phone, DOB, <more>), a vector known as .SNAPD., as well as additional fields. ID Analytics process the data, extract pertinent features and calculate risk score on the fly. The entire process has a sub-second latency. At the basis of our analytics is the ID Network – a virtual graph with SNAPD-vectors as nodes. One can envision making a connection between two nodes if they share some identity element. The weight of the edge is the strength of the connection. As one can imagine various graphical parameters are the predominant inputs to our risk models. At the time I write this, the ID network has 1.5 billion nodes (corresponding to number of transactions); this of course means that the graph is too large to be stored in memory, and needless to say, how we do it is a trade secret, but I will indicate some principles behind the ideas.

Part 2: The risk ID Analytics is scoring falls under the more general rubric of consumer behavior. We are interested in the spatial / temporal aspects of our network and how it related to macroeconomic and social data including demographics, geography, housing, census, interest rates, unemployment, federal deficit, foreign balance of trade and whatnot. Under certain conditions, we will avail our data to an outside organization to participate in publishable research.

Introducing id: a labs, a research-oriented organization which promotes collaborations with academia and other research institutions.

 

Bio: Joseph R. Barr is the Chief Scientist at ID Analytics (www.idanalytics.com). After a few years in academia (as Math/CS Assistant Professor at California Lutheran University,) he has spent the past 17 years in industry as a risk & consumer behavior (analytics) professional. He was awarded a Ph.D. in mathematics from the University of New Mexico on his work on graph colorings, under the direction of Professor Roger C. Entringer. His current interests include the application of statistics, machine-learning and combinatorial algorithms to risk management and consumer behavior. Joe is married, has two young children, a boy and a girl, and an older son, a software engineer at Intel.