Artificial Intelligence-Based Credit Risk Assessment In Banking And Financial Institutions
Artificial Intelligence (AI) has emerged as a transformative technology in the banking and financial sector, enabling institutions to improve the accuracy, speed, and reliability of credit risk assessment. Traditional credit evaluation methods often rely on limited financial indicators and manual decision-making processes, which may result in delayed approvals and inaccurate risk predictions. This study examines the application of AI-based techniques, including Machine Learning (ML), Deep Learning (DL), and predictive analytics, in assessing the creditworthiness of borrowers. AI models analyze large volumes of structured and unstructured data, such as credit history, transaction patterns, income details, repayment behavior, and alternative financial indicators, to identify potential default risks with greater precision. The research highlights how AI-driven credit risk assessment enhances loan approval decisions, minimizes non-performing assets (NPAs), reduces operational costs, and supports financial institutions in maintaining regulatory compliance. Furthermore, the study discusses the challenges associated with AI adoption, including data privacy, algorithmic bias, model interpretability, and cybersecurity concerns. By integrating intelligent risk assessment frameworks into lending operations, banks can strengthen financial stability, improve customer experience through faster loan processing, and achieve more effective risk management. The findings indicate that AI-based credit risk assessment represents a significant advancement over conventional credit evaluation methods and has the potential to reshape the future of banking by promoting efficient, transparent, and data-driven lending decisions. Keywords: Artificial Intelligence (AI), Credit Risk Assessment, Machine Learning, Banking, Financial Institutions, Loan Default Prediction, Predictive Analytics, Credit Scoring, Risk Management, Deep Learning.