Machine Learning for Credit Risk Prediction: A Systematic Literature Review

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 Artificial Intelligence (AI) and ML to assess credit risk, analyzing large volumes of information. We posed research questions about algorithms, metrics, results, datasets, variables, and related limitations in predicting credit risk. In addition, we searched renowned databases responding to them and identified 52 relevant studies within the credit industry of microfinance. Challenges and approaches in credit risk prediction using ML models were identified; we had 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 inquiries, 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 datasets 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 data related to the Demographic, Operation, and Payment behavior. This study aims to guide developers of credit risk management tools and software towards the existing ability of ML methods, metrics, and techniques used to forecast it, thereby minimizing possible losses due to default and guiding risk appetite.

Enhancing Supervised Model Performance in Credit Risk Classification Using Sampling Strategies and Feature Ranking

“…Therefore, for a solid experimental conclusion, another 4-fold cross-validation experiment was also studied. No sampling (original data) Logistic regression 0.9882 (12) 0.9920 (10) 0.9506 (12) 0.9709 (12) 0.9639 (12) Random forest 0.9979 (4) 0.9999 (3) 0.9902 (6) 0.9951 (4) 0.9938 (4) Gradient boosting 0.9961 (9) 0.9999 (3) 0.9812 (10) 0.9905 (9) 0.9882 (9) Over-sampling Logistic regression 0.9914 (11) 0.9895 (12) 0.9685 (11) 0.9789 (11) 0.9736 (11) Random forest 0.9999 (2) 1.0000 (1) 0.9999 (2) 0.9999 (2) 0.9999 (2) Gradient boosting 1.0000 (1) 1.0000 (1) 1.0000 (1) 1.0000 (1) 1.0000 (1) Under-sampling Logistic regression 0.9950 (10) 0.9900 (11) 0.9856 (9) 0.9878 (10) 0.9847 (10) Random forest 0.9986 (3) 0.9989 (8) 0.9940 (3) 0.9965 (3) 0.9956 (3) Gradient boosting 0.9979 (4) 0.9992 (7) 0.9908 (4) 0.9950 (5) 0.9937 (5) Combined sampling Logistic regression 0.9966…”

“…Machine learning techniques have several benefits in various applications, especially in the form of predicting a trend or outcome. Hence, machine learning models can accurately assess credit default probabilities and improve credit risk prediction [1]. Focusing on financial services like personal loans, accurately predicting the risk of non-performing loans (NPLs) in peer-to-peer (P2P) lending is one crucial thing for lenders such as P2P lending platforms.…”

Comparative Machine Learning Models for Predicting Loan Fructification in a Semi-Urban Area

“…As a result, some of the loan received by entrepreneurs does not bear fruit. Many researchers in the existing literature have tried to predict whether or not a person can be granted loan based on certain criteria and characteristics of the person using machine learning (ML) (Guan et al, 2023;Noriega et al, 2023;. Unfortunately, the literature review revealed that there is a lack of research based on the prediction of loan fructification by entrepreneurs.…”