Augmenting Credit Risk Management through AI - Enabled Alternative Data: A New Paradigm for Inclusive Lending and U. S. Economic Advancement

Modern financial systems in the United States exhibit sophisticated lending mechanisms, yet they leave vast segments of potential borrowers ---particularly those with sparse or non -traditional credit histories ---beyond reach. Although legacy creditscoring models have delivered relatively stable outcomes for decades, they frequently overlook promising loan applicants in underserved communities. This paper proposes a transformative approach to U. S. credit risk management, leveraging AI -driven alternative data to better assess borrower risk while expanding equitable access to credit. By incorporating novel datasets such as utility payments, digital transactions, and employment -related signals, we illustrate how machine learning models can provide nuanced, dynamic evaluations of creditworthiness. We also discuss the ethical, regulatory, and technical frameworks necessary for deploying these AI solutions responsibly, ensuring adherence to fair lending laws while maintaining system transparency. Our findings indicate that integrating alternative data within an adaptive AI architecture can fortify credit portfolios, mitigate default risks, and catalyze national economic growth through broad -based financial inclusion.