Data-Driven Approach for Credit Risk Analysis Using C4.5 Algorithm

Credit risk is bad credit, resulting in bank losses due to non-receipt of disbursed funds and unacceptable interest income. However, credit services still have be done achieve profit. The absence an approach that can assist making policies reduce makes the opportunities even more significant. So, data processing techniques are needed produce information used as basis for triggering with mining. research presented application mining a considering ability extract into useful C4.5 algorithm. sample 30 banks 6 factors (credit growth, net margin, type bank, capital ratio, company size, compliance level). was evaluated by decision tree RapidMiner test application. results show growth main factor causing risk, followed level, ratio. Based on obtained, algorithm analyzing easy understand banks.