Farzad Asghari; Farid Ahmadi
Abstract
The aim of this paper is to present a hybrid model to evaluate performance of loan portfolio of banking system regarding loan repayment status and to forecast credit status of loan applicants. At first stage, we have taken credit granting management approach in order to cluster and rank 100,224 loans ...
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The aim of this paper is to present a hybrid model to evaluate performance of loan portfolio of banking system regarding loan repayment status and to forecast credit status of loan applicants. At first stage, we have taken credit granting management approach in order to cluster and rank 100,224 loans granted by Karafarini Omid Fund. All the data on the loans granted to clients was extracted from core banking software of the Fund. Because of having access to this valuable and valid dataset, qualitative data collection methods are not used. In the first section of paper, a type of robust principal component analysis (ROBPCA) was utilized to classify the clients. Then, the eigenvector derived from ROBPCA was used as input to a two-step K-means clustering algorithm. Then, to propose a model to forecast credit status of applicants prior to granting loans, support vector machine (SVM) and artificial genetic neural networks were used. The results obtained from the applicants’ credit status forecasting showed that the model based on the artificial genetic neural networks with the mean-square error of 0.23 and %78 coefficient of determination leads to more accurate forecasting than support vector machine. Therefore, the proposed model for forecasting the applicants’ credit status can predict their performance with relative accurately. A new method in the form of data mining software provides credit institutions with the possibility of predicting applicants’ credit regarding loan repayments.
