Data-driven credit risk monitoring: Leveraging machine learning in risk management

This review explores the evolution of credit risk monitoring, tracing its journey from traditional qualitative assessments to the current integration of machine learning (ML). It highlights how the integration of ML and big data has introduced unprecedented capabilities for analyzing extensive datasets and detecting subtle patterns beyond human capacity. These advanced technologies enable more accurate, efficient and dynamic credit risk predictions through techniques such as random forests, gradient boosting and decision trees. The transformative potential of these methodologies in credit risk assessment was critically examined, addressing challenges such as legacy system integration, data quality and regulatory compliance. It emphasizes the importance of incorporating forward-looking macroeconomic indicators to comply with applicable financial reporting standards and regulatory requirements. Furthermore, it highlights the necessity of ensuring model transparency to maintain trust and compliance. By leveraging the power of big data and ML, the study shows that financial institutions can achieve more precise and proactive risk assessments, enhancing decision-making processes and mitigating potential risks. This comprehensive review provides valuable insights for stakeholders, guiding the implementation of advanced analytical techniques to improve credit risk management. Ultimately, it underscores the potential for a more robust, efficient and stable financial system through the strategic application of ML and big data analytics. Keywords: Credit Risk Management, Financial Services, Machine Learning, Big Data, Financial Transformation.