Risk and risk management in the credit card industry
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Lo_Risk and risk management.pdf
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Author(s)
Butaru, Florentin
Chen, Qingqing
Clark, Brian
Das, Sanmay
Siddique, Akhtar
Lo, Andrew W
Date Issued
August 2016
Journal
Journal of Banking & Finance
Publisher
Elsevier
Citation
Butaru, Florentin; Chen, Qingqing; Clark, Brian; Das, Sanmay; Lo, Andrew W. and Siddique, Akhtar. “Risk and Risk Management in the Credit Card Industry.” Journal of Banking & Finance 72 (November 2016): 218–239. © 2016 The Author(s)
Version
Final published version
Abstract
Using account-level credit card data from six major commercial banks from January 2009 to December 2013, we apply machine-learning techniques to combined consumer tradeline, credit bureau, and macroeconomic variables to predict delinquency. In addition to providing accurate measures of loss probabilities and credit risk, our models can also be used to analyze and compare risk management practices and the drivers of delinquency across banks. We find substantial heterogeneity in risk factors, sensitivities, and predictability of delinquency across banks, implying that no single model applies to all six institutions. We measure the efficacy of a bank's risk management process by the percentage of delinquent accounts that a bank manages effectively, and find that efficacy also varies widely across institutions. These results suggest the need for a more customized approached to the supervision and regulation of financial institutions, in which capital ratios, loss reserves, and other parameters are specified individually for each institution according to its credit risk model exposures and forecasts.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Sloan School of Management
Terms of Use
Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.1016/j.jbankfin.2016.07.015
