Kay Giesecke is Associate Professor of Management Science and Engineering at Stanford University and the Paul Pigott Faculty Scholar in Stanford’s School of Engineering. He directs the Advanced Financial Technologies Laboratory (AFTLab) and co-chairs the Mathematical and Computational Finance Program. He is a member of the Institute for Computational and Mathematical Engineering. He serves on the Governing Board and Scientific Advisory Board of the Consortium for Data Analytics in Risk and on the editorial boards of Mathematical Finance, Operations Research, SIAM Journal on Financial Mathematics, Journal of Risk, and other journals. His research on financial technologies is funded by the National Science Foundation, JP Morgan, State Street, Morgan Stanley, American Express, and other organizations, and has won several prizes. Kay advises a number of financial technology startups and has served as a consultant to banks, investment and risk management firms, governmental agencies, and supranational organizations.

Abstract

Housing affects literally everyone. Mortgages, which constitute the biggest asset class, played a significant role in the financial crisis. In this short talk I will highlight how machine intelligence methods can help address some of the challenging issues that arise in housing finance. Topics will include mortgage delinquency, foreclosure, and prepayment; mortgage-backed and credit-risk-transfer securities; automated house price valuation; and risk capital estimation.