Data-Driven Early-Warning Indicators of Small-Business Financial Distress: Integrating Operational, Transactional, and Credit Data for Decision Support in SMEs

Abstract

Small and medium-sized enterprises remain disproportionately vulnerable to financial distress, yet the periodic financial statements conventionally used to assess their credit health update far too infrequently for meaningful early warning. This article surveys published, cited evidence on integrating operational, transactional, and credit data into data-driven early-warning indicators for SME financial distress, drawing on real, comparative machine-learning results showing multi-layer-perceptron neural networks achieving 95 percent accuracy and a 0.98 ROC-AUC for small-business credit-risk prediction, compared with 79 percent accuracy and a 0.58 AUC for logistic regression on comparable data, with logistic regression correctly identifying only 22 percent of actual defaulters [3]. The article also draws on foundational early-warning literature establishing that the definition of financial distress used to label training data materially affects small-business default-prediction results [5], and proposes an integration framework combining these three data categories into a coherent, decision-useful composite indicator for SME lenders.