The Integration of Artificial Intelligence and Machine Learning in Credit Risk Assessment: An Empirical and Ethical Analysis

Credit risk assessment is a foundational process in the financial industry, historically reliant on subjective judgment and linear statistical models. However, these traditional methods, exemplified by scorecards like FICO, are constrained by their dependence on limited, structured historical data, leading to a failure to accurately evaluate individuals with "thin" or nonexistent credit files. This limitation can perpetuate historical biases and contribute to financial exclusion. The advent of Artificial Intelligence (AI) and Machine Learning (ML) marks a paradigm shift, providing more accurate, efficient, and dynamic tools for assessing creditworthiness. Advanced models, such as Gradient Boosting Machines and Random Forests, consistently outperform traditional techniques by identifying complex, non-linear patterns in vast datasets, including alternative data sources like utility payments and online behavior. While this technological evolution enhances predictive power and financial inclusion, it introduces significant ethical and regulatory challenges, particularly concerning algorithmic bias and the "black box" nature of complex models. Addressing these issues requires the development of transparent, explainable AI (XAI) and adherence to emerging global regulatory frameworks, such as the EU AI Act and the U.S. Equal Credit Opportunity Act. This study presents a comprehensive analysis of this transformative impact, synthesizing current research to outline a methodology for comparative empirical study.