ROLE OF ARTIFICIAL INTELLIGENCE IN CREDIT RISK ASSESSMENT

The rapid advancement of Artificial Intelligence (AI) has significantly transformed the financial services industry, particularly in the area of credit risk management. Traditionally, credit risk assessment relied on statistical models and limited financial data, which often lacked predictive accuracy and adaptability. This study aims to examine the role of AI in enhancing credit risk evaluation by improving decision-making efficiency, accuracy, and risk mitigation. The research adopts a descriptive and analytical methodology, utilizing secondary data from academic journals, financial reports, and industry case studies. AI techniques such as machine learning, neural networks, and predictive analytics are analyzed to understand their application in credit scoring, fraud detection, and default prediction. The findings reveal that AI-based models outperform traditional methods by leveraging large datasets, identifying complex patterns, and enabling real-time risk assessment. Furthermore, AI contributes to financial inclusion by assessing creditworthiness of individuals with limited credit history. However, challenges such as data privacy, model transparency, and ethical concerns remain significant. The study concludes that while AI presents substantial opportunities for improving credit risk management, financial institutions must address these challenges through regulatory compliance and responsible AI practices to ensure sustainable implementation.