Default definition selection for credit scoring

Abstract

In this paper some of the main causes of the recent financial crisis are briefly discussed. Specific attention is paid to the accuracy of credit-scoring models used to assess consumer credit risk. As a result, the optimal default definition selection (ODDS) algorithm is proposed to improve credit-scoring for credit risk assessment.  This simple algorithm selects the best default definition for use when building credit scorecards. To assess ODDS, the algorithm was used to select the default definition for the random forest tree algorithm. The resulting classification models were compared to other models built using the unselected default definitions. The results suggest that the models developed using the default definition selected by the ODDS algorithm were statistically superior to the models developed using the unselected default indicators.