a trust-based credit scoring model using neural network

credit decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. credit scoring models are decision support systems that take a set of predictor variables as input and provide a score as output and creditors use these models to justify who will get credit and who will not. many different credit scoring models have been developed by the banks and researchers in order to solve the classification problems (i.e. distinguishing the good credit customers from the bad ones). almost all these methods categorize the customers into two groups: the good credits and the bad credits. but regarding to the rapid growth in the number of credit applicants and also the intense competition between financial institutions, developing the models which are able to classify credit applicants into more groups (e.g. 6 or more), seems to be necessary. the purpose of this study is to propose an ann- based algorithm which is capable of classifying the customers into 6 levels, regarding to their trust values. till now, almost all of the studies in credit scoring are trying to improve the accuracy rate of the proposed algorithms and this is the first time that trust’s concept is used in credit scoring domain. on the other hand, categorizing customers into more groups, will lead to make fast, easy, certain and fair credit lending decisions.