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

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.