Approaches for Credit Risk Management by Financial Institutions

To ensure prudent decision-making and safeguard their portfolios, financial institutions must adopt robust credit risk management practices. As financial markets expand rapidly, the volume and complexity of credit transactions have increased, prompting a shift from traditional rule-based systems to data-driven approaches. This paper examines how advanced techniques—particularly machine learning, artificial intelligence, statistical modelling, and big data analytics—enhance credit risk assessment and default prediction.

The study highlights the transition from classical credit scoring models to modern approaches such as decision trees, support vector machines, neural networks, and deep learning algorithms, demonstrating substantial improvements in predictive performance. Furthermore, the integration of alternative data sources, including transaction histories, mobile payment records, social media activity, and behavioural indicators, enables a more comprehensive evaluation of borrower risk profiles, especially for individuals with limited credit histories.

The paper also discusses regulatory considerations associated with adopting data-driven models, emphasizing the importance of transparency, fairness, and explainability. While these approaches improve predictive accuracy and operational efficiency, challenges such as data privacy concerns, algorithmic bias, high infrastructure costs, and data governance issues remain. Overall, this study provides a comprehensive overview of the transformative potential of data-driven credit risk management and outlines key implications for future research and innovation.