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

Abstract

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

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“…Meanwhile, sequencing, often seen as an extension of association, serves to represent the plural form of association. Technically, it identifies different relationships within a specified period from the obtained data [44].…”