Introducing Time-Changing Economics into Credit Scoring

We propose a two-stage model for dealing with the temporal degradation of credit scoring models. First, we develop a model from a classical framework, with a static supervised learning setting and binary output. Then, we introduce the time-changing economic factors, using a regression between the macroeconomic data and the internal default in the portfolio. In so doing, the specific risk is captured from the bank internal database, and the movement of systemic risk is determined with the regression. This methodology produced motivating results in a 1-year horizon, for a portfolio of customers with credit cards in a financial institution operating in Brazil. We anticipate that it can be extended to other applications of risk assessment with great success. This methodology can be further improved if more information about the economic cycles is integrated in the forecasting of default.