Support vector machines for credit scoring: Extension to non standard cases

Credit scoring is being used in order to assign credit applicants to good and bad risk classes. This paper investigates the credit scoring performance of support vector machines (SVM) with weighted classes and moderated outputs. First, we consider the adjustment of support vector machines for credit scoring to a set of non standard situations important to practitioners. Such more sophisticated credit scoring systems will adapt to vastly different proportions of credit worthiness between sample and population. Different costs for different types of misclassification will also be handled. Second, sigmoid output mapping is used to derive default probabilities, important for constructing rating systems and a step towards more "personalized" credit contracts.