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Risk and risk management in the credit card industry

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Listed:
  • Butaru, Florentin
  • Chen, Qingqing
  • Clark, Brian
  • Das, Sanmay
  • Lo, Andrew W.
  • Siddique, Akhtar

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.

Suggested Citation

  • Butaru, Florentin & Chen, Qingqing & Clark, Brian & Das, Sanmay & Lo, Andrew W. & Siddique, Akhtar, 2016. "Risk and risk management in the credit card industry," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 218-239.
    • Handle: RePEc:eee:jbfina:v:72:y:2016:i:c:p:218-239
    DOI: 10.1016/j.jbankfin.2016.07.015
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    1. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
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    3. Glennon, Dennis & Kiefer, Nicholas M. & Larson, C. Erik & Choi, Hwan-sik, 2007. "Development and Validation of Credit-Scoring Models," Working Papers 07-12, Cornell University, Center for Analytic Economics.
    4. S. le Cessie & J. C. van Houwelingen, 1992. "Ridge Estimators in Logistic Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 191-201, March.
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    More about this item

    Keywords

    Credit risk; Consumer finance; Credit card default model; Machine-learning;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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