AI-Augmented Financial Ratio Analysis: Enhancing Credit Risk Assessment for SMES with Non-Traditional Data

ABSTRACT: The traditional dependence on financial ratio analysis to evaluate the creditworthiness of Small and Medium Enterprises (SMEs) is facing new challenges from data-driven innovations. Standard models often struggle due to limited financial information, inconsistent record-keeping, and the natural instability of SMEs. As artificial intelligence (AI) grows and access to non-traditional data sources increases, such as transaction histories, digital footprints, and behavioral patterns, credit risk assessment can move beyond traditional methods. This study looks into how combining AI-driven insights from alternative data with standard financial ratios can improve SME credit risk assessment. By using a mixed-methods approach, the research creates and tests AI models that include both structured financial metrics and unstructured alternative data. It also examines the ethical, operational, and regulatory impacts of AI-enhanced credit evaluation systems. The findings aim to greatly help financial inclusion, increase the accuracy of credit assessments, and provide scalable solutions for lenders operating in the SME credit markets of a more digital economy.