Özet:

Credit risk is one of the major risks faced by commercial banks and has an important effect on profitability ratios. Consequently, the credit scoring system in decision making for banks and other financial institutions lending to consumers is very important. Financial institutions constitute various internal credit assessment models reveal relationships between variables affecting credit scoring. In the literature, statistics and machine learning techniques for credit ratings have been widely studied. In this study, particularly for banks, financial institutions and customers of these institutions are discussed, including issues that are important to determine customer’s credit score. Customer’s credit score is a term used to allocate credit using statistical techniques and methods of machine learning techniques. Such methods have become increasingly more important in recent years with the growth in commercial loans. Although a wide range of applied statistical methods included in the literature, they are limited because of commercial confidentiality. In this study, for issuing the decision on the assessment of customers’ demand for credit, acceptance of loan requests or refusal, a software model based on the artificial neural network (ANN) methodology is recommended. A real data set belongs to a deposit bank was used for the application. In addition to this, this study benchmarks the performance of ANN model with decision trees (DT) model. Based on the findings, ANN model outperforms the DT model in terms of estimating credit risk and customer’s credit score. It is also considered that the model would be helpful in increasing the profitability of lenders gained from credits

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