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In the rapidly evolving financial landscape, effectively managing credit risk is crucial for the stability and profitability of financial institutions. Traditional methods of credit risk management, which rely heavily on statistical models and expert judgment, are being transformed by the advent of artificial intelligence (AI) and machine learning (ML). These technologies are introducing unprecedented accuracy and efficiency into the risk assessment processes, making them indispensable tools for modern financial institutions. A recent publication, "Machine Learning for Credit Risk Prediction: A Systematic Literature Review," authored by Jomark Pablo Noriega, Luis Antonio Rivera, and Jose Alfredo Herrera, offers a comprehensive analysis of the current state of ML applications in credit risk prediction. The review synthesizes findings from 52 relevant studies, providing a detailed overview of the most effective ML models, key performance metrics, and the challenges and opportunities associated with implementing these technologies. The review identifies that machine learning models, particularly those in the boosted category, such as Gradient Boosting Machines (GBM) and XGBoost, have emerged as leading techniques in credit risk prediction due to their superior ability to handle large datasets and complex variable interactions. These models have demonstrated remarkable performance when evaluated using metrics such as the Area Under Curve (AUC), Accuracy (ACC), Recall, Precision, and the F1 Score, making them highly effective tools for predicting credit risk. However, deploying ML models has its challenges. The inherent "black box" nature of many ML algorithms poses significant interpretability issues, hindering trust and regulatory acceptance. Addressing these concerns requires the development of more transparent and explanatory AI systems. Additionally, selecting relevant features, managing multicollinearity, and dealing with imbalanced datasets remain critical areas needing further research and refinement. The review also highlights several future research directions to enhance the applicability of ML in credit risk management. These include improving model interpretability, enhancing data quality and diversity, and integrating alternative data sources such as social media and transaction data to create more comprehensive and fair credit scoring systems. The findings from this review are particularly pertinent for financial institutions in Africa, where the rapid adoption of fintech solutions is driving significant advancements in financial inclusion and risk management. By leveraging ML technologies, these institutions can enhance their predictive capabilities and foster a more inclusive financial ecosystem.
Abikoye et al. (Fri,) studied this question.