Implementing a credit risk management system based on innovative scoring techniques

Abstract

In recent years, most developed countries have suffered a severe recession due to a financial crisis starting in the US with mortgages loans. The lack of credit risk management has been pointed out as one of the causes of this bank panics. To avoid a similar situation, the credit card companies need to have proper risk management tools. This thesis presents a credit scoring system which aims at setting credit lines and thus, controlling credit risk. It includes three types of models: application scorecards, early detection scorecards and behavioral scorecards. They have been built on real and recent data coming from a German credit card company. The models have been built with a training sample and validated accordingly, using logistic regression. Information value and validation charts have been used for comparing the models. In the scoring process described, the scorecards are used in a sequential order. The author shows that minimizing losses might not be optimal in order to maximize profit. Finally, the author presents possible extensions to the research. The author hopes that the microeconomic analysis of the mechanics of a particular lender’s credit allocation process described in this thesis can play some part in preventing future financial crisis.