Risk and Risk Management in the Credit Card Industry

62 Pages Posted: 16 Jun 2015 Last revised: 14 Jun 2016

See all articles by Florentin Butaru

Florentin Butaru

Government of the United States of America - Office of the Comptroller of the Currency (OCC)

Qingqing Chen

Government of the United States of America - Office of the Comptroller of the Currency (OCC)

Brian J. Clark

Rensselaer Polytechnic Institute (RPI); Office of the Comptroller of the Currency

Sanmay Das

Washington University in St. Louis

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER); Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

Akhtar R. Siddique

Government of the United States of America - Risk Analysis Division

Multiple version iconThere are 2 versions of this paper

Date Written: June 12, 2016

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 the 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.

Keywords: credit risk, consumer finance, credit cards, risk management, machine learning, big data

JEL Classification: G21, G28, D12, E44, C14, C45, C53

Suggested Citation

Butaru, Florentin and Chen, Qingqing and Clark, Brian J. and Das, Sanmay and Lo, Andrew W. and Siddique, Akhtar R., Risk and Risk Management in the Credit Card Industry (June 12, 2016). Available at SSRN: https://ssrn.com/abstract=2618746 or http://dx.doi.org/10.2139/ssrn.2618746

Florentin Butaru

Government of the United States of America - Office of the Comptroller of the Currency (OCC) ( email )

400 7th Street SW
Washington, DC 20219
United States

Qingqing Chen

Government of the United States of America - Office of the Comptroller of the Currency (OCC) ( email )

400 7th Street SW
Washington, DC 20219
United States

Brian J. Clark

Rensselaer Polytechnic Institute (RPI) ( email )

Troy, NY 12180
United States

Office of the Comptroller of the Currency ( email )

400 7th Street SW
Washington, DC 20219
United States
202-215-6500 (Phone)

Sanmay Das

Washington University in St. Louis ( email )

One Brookings Drive
Campus Box 1208
St. Louis, MO MO 63130
United States

Andrew W. Lo (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-618
Cambridge, MA 02142
United States
617-253-0920 (Phone)
781 891-9783 (Fax)

HOME PAGE: http://web.mit.edu/alo/www

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

Stata Center
Cambridge, MA 02142
United States

Akhtar R. Siddique

Government of the United States of America - Risk Analysis Division ( email )

400 7th Ave
Washington, DC 20219
United States
202-649-5526 (Phone)

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