Filiz, GozdeBodur, TolgaYaslidag, NihalSayar, AlperenÇakar, Tuna2024-09-082024-09-082024979835038897897983503889612165-0608https://doi.org/10.1109/SIU61531.2024.10601148https://hdl.handle.net/20.500.11779/2337This study examines the transformation in the financial services sector, particularly in banking, driven by the rapid development of technology and the widespread use of big data, and its impact on credit prediction processes. The developed credit prediction model aims to more accurately predict customers' credit repayment capacities. In pursuit of this goal, demographic and financial data along with credit histories of customers have been utilized to employ data preprocessing techniques and test various classification algorithms. Findings indicate that models developed with XGBoost and CATBoost algorithms exhibit the highest performance, while the effective use of feature engineering techniques is revealed to enhance the model's accuracy and reliability. The research highlights the potential for financial institutions to gain a competitive advantage in risk management and customer relationship management by leveraging machine learning models.trinfo:eu-repo/semantics/closedAccessCredit Prediction ModelsMachine LearningRisk PredictionConference Object10.1109/SIU61531.2024.106011482-s2.0-85200887297