Machine Learning-Based Credit Risk Prediction: A Systematic Review of Techniques, Challenges, and Future Directions

Credit risk prediction is a crucial process in financial institutions, aiming to evaluate the likelihood of borrowers defaulting on their loan obligations. Accurate credit risk prediction significantly impacts financial stability and profitability by minimizing default risks and associated losses (Thomas, Edelman, Chen & Guestrin, 2016). However, challenges such as data quality, feature selection complexities, model interpretability, and scalability persist in real-world applications. Future research should focus on developing hybrid models, real-time predictive capabilities, and enhancing model interpretability to improve their applicability in financial decision-making (Addo, Guegan, & Hassani, 2018).