Enhancing U.S. Small Business Lending Through Predictive AI Credit Scoring A Framework for Reducing Default Risk and Strengthening Economic Stability

Abstract:Accurately assessing credit risk for small businesses has long been a challenge for financial institutions. Traditional credit scoring methods, which rely primarily on historical financial statements and limited credit history, often fail to capture the dynamic and multifaceted nature of small business operations. These limitations increase default probabilities, constrain access to financing for high-potential entrepreneurs, and reduce the efficiency of capital allocation.This paper introduces a predictive AI credit scoring framework designed to enhance small business lending in the United States. The framework employs advanced machine learning techniques—including gradient boosting, ensemble modelling, and anomaly detection—in combination with alternativeand multidimensional data sources, such as cash-flow patterns, transactional behaviour, digital footprints, industry-specific risk indicators, and macroeconomic trends. By integrating these diverse inputs, the model provides robust, real-time, and multidimensional assessments of borrower risk, improves early detection of financial distress, and reduces misclassification errors compared to conventional scoring systems.Empirical evaluation demonstrates that the predictive AI framework improves portfolio stability, lowers default probabilities, and expands access to credit for previously underserved businesses. The study further highlights the broader economic and policy implications for the United States: improved credit accuracy facilitates financial inclusion, supports entrepreneurial growth, strengthens systemic stability, and promotes more efficient capital allocation across the small business sector.The findings indicate that predictive AI credit scoring serves as a strategic tool for the U.S. economy, enabling financial institutions, fintech platforms, and policymakers to make more informed, timely, and equitable lending decisions, thereby fostering a resilient and inclusive small business ecosystem while contributing to long-term economic growth.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-30
DOI
https://doi.org/10.5281/zenodo.17940059
Primary Topic
Financial Distress and Bankruptcy Prediction
Type
article
Field-Weighted Citation Impact
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article

Enhancing U.S. Small Business Lending Through Predictive AI Credit Scoring A Framework for Reducing Default Risk and Strengthening Economic Stability

Zenodo (CERN European Organization for Nuclear Research)
Financial Distress and Bankruptcy Prediction
article

Enhancing U.S. Small Business Lending Through Predictive AI Credit Scoring A Framework for Reducing Default Risk and Strengthening Economic Stability

article en

Abstract

Abstract:Accurately assessing credit risk for small businesses has long been a challenge for financial institutions. Traditional credit scoring methods, which rely primarily on historical financial statements and limited credit history, often fail to capture the dynamic and multifaceted nature of small business operations. These limitations increase default probabilities, constrain access to financing for high-potential entrepreneurs, and reduce the efficiency of capital allocation.This paper introduces a predictive AI credit scoring framework designed to enhance small business lending in the United States. The framework employs advanced machine learning techniques—including gradient boosting, ensemble modelling, and anomaly detection—in combination with alternativeand multidimensional data sources, such as cash-flow patterns, transactional behaviour, digital footprints, industry-specific risk indicators, and macroeconomic trends. By integrating these diverse inputs, the model provides robust, real-time, and multidimensional assessments of borrower risk, improves early detection of financial distress, and reduces misclassification errors compared to conventional scoring systems.Empirical evaluation demonstrates that the predictive AI framework improves portfolio stability, lowers default probabilities, and expands access to credit for previously underserved businesses. The study further highlights the broader economic and policy implications for the United States: improved credit accuracy facilitates financial inclusion, supports entrepreneurial growth, strengthens systemic stability, and promotes more efficient capital allocation across the small business sector.The findings indicate that predictive AI credit scoring serves as a strategic tool for the U.S. economy, enabling financial institutions, fintech platforms, and policymakers to make more informed, timely, and equitable lending decisions, thereby fostering a resilient and inclusive small business ecosystem while contributing to long-term economic growth.

Zenodo (CERN European Organization for Nuclear Research)
National Australia Bank (AU)
Reduced inequalities
Openalex Percentile: Top 26%
Financial Distress and Bankruptcy Prediction
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Enhancing U.S. Small Business Lending Through Predictive AI Credit Scoring A Framework for Reducing Default Risk and Strengthening Economic Stability · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS