Quantifying the Prospective Impact of AI-Assisted Early Intervention in U.S. Small-Business Lending: Default Avoidance, Credit-Access Preservation, and Small-Business Employment Effects Across Community Lending Portfolios

Abstract

Small businesses account for 45.9% of U.S. private-sector employment and generated 88.9% of net new jobs between March 2023 and March 2024, making the health of small-business credit markets a matter of direct macroeconomic consequence, not merely a lending-industry concern [3][4]. This article builds a transparent, assumption-explicit model quantifying the prospective employment impact of AI-assisted early-intervention systems of the kind addressed throughout this article series applied to the SBA 7(a) loan portfolio, which guaranteed $37 billion across 77,600 loans in FY2025 alone [5]. Combining the SBA portfolio's historical 3–6% default rate with Brown and Earle's (2017) peer-reviewed empirical estimate that SBA lending produces 3 to 4 jobs per $1 million lent, this article models the employment effect of avoided defaults under a range of default-reduction scenarios, rather than asserting a single, unsupported point estimate. Under an illustrative 10% relative reduction in 7(a) default rates a conservative scenario relative to the 30%+ reductions reported in some early-intervention pilot studies discussed elsewhere in this series the model estimates approximately 583 jobs preserved annually from avoided defaults on the FY2025 7(a) portfolio alone; a 30% reduction scenario estimates approximately 1,748 jobs preserved. These figures should be read as a transparent modelling exercise built from published component estimates, not as a validated empirical finding, since no study has yet directly measured the employment effect of AI-assisted early intervention specifically. This article makes every assumption in the model explicit so that a reader can substitute their own inputs and evaluate the model's sensitivity to each one.