Impact of Artificial Intelligence and Business Intelligence Applications on SME Credit Risk Assessment in the United States
TL;DR
Findings reveal that AI and BI significantly enhance the precision, speed, and objectivity of credit risk assessments, enabling improved identification of high-risk borrowers and reducing subjective biases.
Abstract
This study investigates the impact of Artificial Intelligence (AI) and Business Intelligence (BI) applications on the credit risk assessment of Small and Medium Enterprises in the United States. The study used a structured questionnaire with closed-ended questions. Utilizing a quantitative survey of financial professionals across diverse U.S. lending institutions, the study employs the Technology-Organization-Environment (TOE) framework and Information Asymmetry Theory to analyze empirical data. Findings reveal that AI and BI significantly enhance the precision, speed, and objectivity of credit risk assessments, enabling improved identification of high-risk borrowers and reducing subjective biases. Institutional readiness, technological infrastructure, skilled personnel, and regulatory alignment emerge as critical enablers, while challenges such as data fragmentation, capital constraints, and model explainability persist. The study contributes to the fintech literature by validating theoretical models through empirical evidence and offers practical insights for policymakers and financial institutions aiming to optimize SME lending processes, promote innovation, and foster economic growth.
