What type of study is this?

This is a Systematic Review study.

September 16, 2025

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

Key Points

Machine learning techniques, notably ensemble methods like Random Forest and XGBoost, improve credit risk prediction accuracy compared to traditional models.
Challenges in data quality, feature selection, and model interpretability continue to impact the adoption of machine learning in credit risk assessment.
Future research must focus on hybrid models and developing real-time predictive capabilities to enhance decision-making in financial contexts.
Ultimately, better machine learning models in credit risk prediction can significantly contribute to financial stability and profitability.

Abstract

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).

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

Key Points

Abstract

Cite This Study