Consumer Credit-Risk Models Via Machine-Learning Algorithms

We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data from January 2005 to April 2009 for a sample of a major commercial bank’s customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults, with linear regression R2’s of forecasted/realized delinquencies of 85%. Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses. Moreover, the time-series patterns of estimated delinquency rates from this model over the course of the recent financial crisis suggest that aggregated consumer credit-risk analytics may have important applications in forecasting systemic risk.Massachusetts Institute of Technology. Laboratory for Financial EngineeringMassachusetts Institute of Technology. Center for Future Bankin

Stress testing credit card portfolios: an application in South Africa

Motivated by a real problem, this study aims to develop models to conduct stress testing on credit card portfolios. Two modelling approaches were extended to include the impact of lenders’ actions within the model. The first approach was a regression model of the aggregate losses based on economic variables with autocorrelations of the errors. The second approach was a set of vintage-level models that highlighted the months-on-book effect on credit losses. A case study using the models was described using South African credit card data. In this case, the models were used to stress test the credit card portfolio under several economic scenarios

Monetary policy report to the Congress, March 1, 2011

Monetary policy - United States ; Economic conditions - United States

Forecasting and Forecast Combination in Airline Revenue Management Applications

Predicting a variable for a future point in time helps planning for unknown future situations and is common practice in many areas such as economics, finance, manufacturing, weather and natural sciences. This paper investigates and compares approaches to forecasting and forecast combination that can be applied to service industry in general and to airline industry in particular. Furthermore, possibilities to include additionally available data like passenger-based information are discussed

Integrating credit and interest rate risk: A theoretical framework and an application to banks' balance sheets

Credit and interest rate risk in the banking book are the two most important risks faced by commercial banks. In this paper we derive a consistent and general framework to measure the riskiness of a bank which is subject to correlated interest rate and credit risk. The framework accounts for all sources of credit risk, interest rate risk and their combined impact As we model the whole balance sheet of a bank the framework not only enables us to assess the impact of credit and interest rate risk on the bank's economic value but also on its future earnings and capital adequacy. We apply our framework to a hypothetical bank in normal and stressed conditions. The simulation highlights that it is fundamental to measure the impact of correlated interest rate and credit risk jointly on the whole portfolio of banks, including assets, liabilities and off-balance sheet itemsIntegration of credit risk & interest rate risk, asset & liability management of banks, economic value, stress testing