Areas of Expertise (10)
Credit Cards
Information Economics
Probability and Statistics
System Dynamics
Pricing
Revenue Management
Optimal Control of Nonlinear Systems
Economics of Uncertainty
Stochastic Processes
Corporate Strategy
Biography
Assistant Professor Naveed Chehrazi joined the Department of Information, Risk, and Operations Management in July 2013 upon receiving his Ph.D. in management science and engineering at Stanford University. Chehrazi's doctoral dissertation and his current research focus on consumer behavior in the unsecured debt market.
Chehrazi's research combines tools from different disciplines including statistics, economics, and operations research to develop mathematical models that can explain and predict consumers' behavior in terms of their repayment pattern when they are in default.
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Education (3)
Stanford University: Ph.D., Management Science and Engineering 2013
Thesis: "Identification and Optimization of Stochastic Systems".
Stanford University: M.S., Management Science and Engineering 2008
Sharif University of Technology: B.Sc., Electrical Engineering 2005
Media Appearances (2)
New Model Predicts Delinquent Card Repayment
Banking Exchange online
2015-05-26
University researchers claim to have identified a way to accurately predict which delinquent credit card accounts will repay an outstanding balance. This could help decisionmakers choose among methods of recovering past dues and chargeoffs.
Model Predicts Which Delinquent Credit Card Holders Will Pay
UT News online
2015-05-14
McCombs Assistant Professor Naveed Chehrazi and his co-author Thomas Weber from École Polytechnique Fédérale de Lausanne in Switzerland worked directly with a major credit card issuer to develop the Dynamic Collectability Score, which ranks delinquent account holders based on factors such as size of outstanding balance, mortgage status, past payment history, credit score, and external factors such as the performance of the stock market and the national unemployment rate.
Articles (2)
Dynamic Valuation of Delinquent Credit-Card Accounts
Social Science Research Network
2015-02-15
This paper introduces a dynamic model of the stochastic repayment behavior exhibited by delinquent credit-card accounts. Based on this model, we construct a dynamic collectability score (DCS) which estimates the account-specific probability of collecting a given portion of the outstanding debt over any given time horizon. The model integrates a variety of information sources, including historical repayment data, account-specific, and time-varying macroeconomic covariates, as well as scheduled account-treatment actions.
Monotone Approximation of Decision Problems
Operations Research
2010-07-07
Many decision problems exhibit structural properties in the sense that the objective function is a composition of different component functions that can be identified using empirical data. We consider the approximation of such objective functions, subject to general ...
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