Recovery Rate Modelling of Non-performing Consumer Credit Using Data Mining Algorithms

There have been more studies on recovery rate modeling of bonds than of personal loans and retail credit. As far as the authors are aware, there exists no research of recovery rate modeling in retail credit for third-party buyers. The goal of this paper is to ll this gap. In our study, over nine million defaulted or non-performing consumer credit data provided by a German debt collection company are used. According to the ndings, the optimum times of the collection processes are not the same for all industries. Moreover, from a variety of characteristics, those debtor characteristics that are most signi cant in predicting the recovery have been determined. To select the best prediction and classi cation model, a variety of statistical and data mining methods such as logistic regression, neural network, K-nearest neighbor, CHAID, CART, Support Vector Machine and regression will be examined. A two-stage model which rst classi es debts to extreme and non-extreme recovery rate is applied; then, the extreme debts are classi ed into full payment and non-payment. Moreover, the non-extreme recovery rates are predicted.