AI-Powered Credit Risk Assessment: A Paradigm Shift in Financial Decision-Making
Abstract
The integration of artificial intelligence (AI) in credit risk assessment has transformed the financial sector by enabling more accurate, efficient, and scalable decision-making processes. This paper explores various AI techniques, including machine learning models such as decision trees, random forests, and neural networks, to evaluate creditworthiness. Using real-world datasets, the study compares AI-driven methods with traditional statistical models, highlighting improvements in prediction accuracy and operational efficiency. Challenges such as data privacy, regulatory compliance, and algorithmic bias are addressed, and strategies for overcoming these obstacles are proposed. The findings underscore AI's potential to redefine risk assessment in the financial industry.
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