This research aims to implement the C4.5 algorithm in predicting bad credit in digital loan systems in the FinTech industry. The C4.5 algorithm was chosen because of its ability to handle numeric and categorical attributes, as well as produce a decision tree that can be interpreted easily. This research uses a dataset containing customer transaction and profile information, such as employment status, income and payment history. Test results show that the C4.5 algorithm is able to achieve an accuracy of 89.6% in predicting the possibility of bad credit, so it can help FinTech companies manage credit risk more effectively.
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