Risk and Risk Management in the Credit Card Industry

By F. Butaru, Qingqing Chen, Brian J. Clark, Sanmay Das, Andrew W. Lo, Akhtar Siddique. 2016

235 citations recorded by the source corpus.

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.

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Citations in the corpus

Recurrent Neural Networks Approach to the Financial Forecast of Google Assets (2017, neutral): “Such type of models can be also applied within the risk management scenario, where risky assets, see [14], can be classified in supervised way by mean of classical machine learning algorithms as random forests, or by using complex classifiers, as deep Nns.”
Empirical Asset Pricing via Machine Learning (2018, unknown): “(2010) and Butaru et al. (2016) use regression trees to predict consumer credit card delinquencies and defaults. Sirignano et al. (2016) estimate a deep neural network for mortgage prepayment, delinquency, and foreclosure. Heaton et al. (2016) develop a deep learning neural network routine to automa”
Predicting mortgage default using convolutional neural networks (2018, background): “Butaru et al. (2016) also include macroeconomic variables, such as interest rates and unemployment statistics, as an attempt to make the delinquency model generalize better over longer periods of time. As all these papers use similar explanatory variables, the researchers commonly explore difference”