CREDIT RISK ASSESSMENT USING MACHINE LEARNING

Abstract

Effective credit risk assessment is paramount for maintaining financial stability and fostering economic resilience. Leveraging machine learning (ML) techniques offers promising avenues for enhancing the accuracy and efficiency of credit risk evaluation. This paper presents a mixed-methods approach integrating quantitative analysis and qualitative insights to develop a comprehensive methodology for credit risk assessment using ML. Drawing from a review of contemporary literature and insights from industry experts, we propose a structured framework encompassing data collection, preprocessing, model selection, feature engineering, training, evaluation, interpretation, and validation. The methodology emphasizes the importance of selecting appropriate ML algorithms, feature engineering techniques, and model evaluation metrics tailored to the nuances of credit risk assessment. Comparative analysis with traditional statistical approaches underscores the superiority of ML models in predictive accuracy and robustness. Ethical considerations regarding data privacy, bias mitigation, and regulatory compliance are also addressed. The key findings highlight the potential of ML-driven credit risk assessment in enabling proactive risk management and fostering financial inclusivity. Recommendations for financial institutions include integrating ML models into existing risk assessment frameworks and investing in data governance infrastructure to ensure the ethical and responsible use of customer data. Avenues for future research include exploring advanced ML techniques such as deep learning and reinforcement learning for credit risk assessment.