Noriega, J.P.; Rivera, L.A.; Herrera, J.A. Machine Learning for Credit Risk Prediction: A Systematic Literature Review. Data2023, 8, 169.
Noriega, J.P.; Rivera, L.A.; Herrera, J.A. Machine Learning for Credit Risk Prediction: A Systematic Literature Review. Data 2023, 8, 169.
Noriega, J.P.; Rivera, L.A.; Herrera, J.A. Machine Learning for Credit Risk Prediction: A Systematic Literature Review. Data2023, 8, 169.
Noriega, J.P.; Rivera, L.A.; Herrera, J.A. Machine Learning for Credit Risk Prediction: A Systematic Literature Review. Data 2023, 8, 169.
Abstract
In this systematic review of the literature on using Machine Learning (ML) for credit risk prediction, we raise the need for financial institutions to use AI and ML to assess credit risk, analyzing large volumes of information. We posed research questions about algorithms, metrics, results, data sets, variables, and related limitations in predicting credit risk. We searched renowned databases to answer them and identified 52 relevant studies with the credit industry microfinance. Challenges and approaches in credit risk prediction using ML models we identified, difficulties with the implemented models such as the black box model, the need for explanatory artificial intelligence, the importance of selecting relevant features, addressing multicollinearity, and the problem of the imbalance in the input data. By answering the questions, we identified that the Boosted Category is the most researched family of ML models; the most commonly used metrics for evaluation are Area Under Curve (AUC), Accuracy (ACC), Recall, precision measure F1 (F1), and Precision; Research mainly uses public data sets to compare models, and private ones to generate new knowledge when applied to the real world. The most significant limitation identified is the representativeness of reality, and the variables primarily used in the microcredit industry are related data to the demographic, the operation, and payment behavior. This study aims to guide the developers of credit risk management tools and software towards the existing offer of ML methods, metrics, and techniques used to forecast it, thereby minimizing possible losses due to default and guiding risk appetite.
Keywords
loan; credit risk; prediction; machine learning; systematic literature review
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
