A survey of financial professionals at U.S. lending institutions finds that AI and BI applications improve the precision, speed, and objectivity of SME credit risk assessment and better identify high-risk borrowers

The study finds that AI and BI applications significantly enhance the precision, speed, and objectivity of SME credit risk assessment, improving identification of high-risk borrowers and reducing subjective biases. Prior literature often discusses AI in credit scoring from a theoretical or single-algorithm experimental angle; this work places AI and BI together within one assessment framework and tests it empirically with questionnaire data from financial professionals at U.S. lending institutions. Evidence comes from a quantitative survey using a structured closed-ended questionnaire with financial professionals across diverse U.S. lending institutions, analyzed through the TOE framework and Information Asymmetry Theory; the abstract does not report sample size, effect sizes, or statistical test details.

Institutional readiness, technological infrastructure, skilled personnel, and regulatory alignment are identified as critical enablers of AI and BI adoption. The paper presents these organizational and environmental conditions as empirical findings rather than leaving the discussion at technical feasibility, linking adoption to institutional capability and the regulatory environment. Based on empirical analysis of the same questionnaire data, organized along the organizational and environmental dimensions of the TOE framework; the abstract does not report relative weights or significance levels for each factor.

Data fragmentation, capital constraints, and model explainability are identified as persistent challenges. While affirming the performance gains of AI and BI, the study also identifies implementation obstacles, offering an empirical view of the tension between technological benefits and practical constraints. Derived from questionnaire responses of the surveyed financial professionals; the abstract does not state how prevalent these challenges are or their quantitative distribution.

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. It applies both the TOE framework and Information Asymmetry Theory to the SME credit risk assessment context, combining theoretical validation and practical recommendations in one study. This is the authors' statement of theoretical contribution and practical implications based on the questionnaire findings; the abstract does not provide specific policy recommendations or validation metrics.

The study targets financial professionals engaged in credit risk assessment at U.S. lending institutions and is suited to understanding institution-level adoption and perceived effects of AI and BI in SME credit assessment. Its conclusions can serve managers of financial institutions seeking to optimize SME lending processes and policymakers concerned with fintech and credit access. Because the study uses the TOE framework and Information Asymmetry Theory as its analytical lens, its scope centers on how organizational and environmental factors shape the effects of technology application.

Because the current reading scope is incomplete, with only the abstract and references visible, the questionnaire sample size, sampling method, reliability and validity checks, statistical models, and effect sizes are not available, so the specific strength and robustness of the reported 'significant' improvements remain open questions. In addition, the prevalence and relative importance of challenges such as data fragmentation, capital constraints, and model explainability, as well as differences across lending institutions of different sizes and types, would need to be confirmed in the full-text data.