Assessing Credit Risk: An Application of Data Mining in a Rural Bank

Abstract

Abstract Credit risk assessment for secured loans is an important operation in banking systems to ensure the lenders pay the loans on schedule and to classify the bank as a well performing bank due to regulation. This paper aims to identify factors which are necessary for a rural bank (Bank Perkreditan Rakyat) to assess credit application. By aiming on the reduction of number of non-performing loans, current decision criteria on credit risk assessment are evaluated. Subsequently, a decision tree model is proposed by applying data mining methodology. The credit risk assessment model is applied to PT BPR X in Bali that had 1082 lenders (11.99%) who had non-performing loans and were identified as bad loan cases. This made PT BPR X was categorized as a poorly performing bank. Data mining is used to suggest a decision tree model for credit assessment as it can indicate whether the request of lenders can be classified as performing or non-performing loans risk. Using C 5.0 methodology, a new decision tree model is generated. This model suggests that new criteria in analyzing the loan application. The evaluation results show that if this model is applied, PT BPR X can reduce non-performing loans to less than 5% and the bank can be classified as a well performing bank.

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