Comparison of classification techniques used for credit risk assessment in financial modeling

Credit risk refers to the risk that a borrower will default on any type of debt by failing to make payments which it is obligated to do. Assessment of Credit Risk is very important for any type of financial institution for avoiding huge amount of losses that may be associated with any type of inappropriate credit approval decision. In this paper, we are going to compare different classification techniques used for credit risk assessment such as linear discriminant analysis, logistic regression, classification and regression tree, support vector machine, neural network and genetic algorithm.