Working Paper: NBER ID: w21305
Authors: Florentin Butaru; Qingqing Chen; Brian Clark; Sanmay Das; Andrew W. Lo; Akhtar Siddique
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: Risk Management; Credit Cards; Machine Learning; Delinquency Prediction
JEL Codes: D12; D14; D18; E21; E51; G01; G17; G21
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| effective risk management practices (H12) | better outcomes in terms of delinquency prediction (C52) |
| better forecasting of delinquent accounts (G17) | better risk management outcomes (G11) |
| risk management practices (G22) | delinquency rates (G33) |
| heterogeneity in risk factors' sensitivities (C21) | predictability of delinquency (K42) |