AI-Driven Accounts Payable and Receivable Automation for Operational Risk Mitigation in U.S. SMEs: A Systematic Review (2018–2026)

Abstract

This study examined the problem of operational risk exposure in U.S. small and medium-sized enterprises caused by manual, fragmented, or weakly integrated accounts payable and accounts receivable workflows. The purpose was to determine whether AI-driven AP/AR automation reduces risks related to invoice errors, duplicate payments, delayed collections, reconciliation mismatches, fraud exposure, weak audit trails, and limited cash flow visibility. Using a quantitative, cross-sectional, case-based design, the study collected survey data from 220 valid respondents drawn from cloud-based and enterprise financial automation cases in U.S. SMEs, including SME owners, finance managers, accountants, bookkeepers, controllers, operations managers, and AP/AR staff. The key variables were accounts payable automation, accounts receivable automation, AI-enabled invoice processing, AI-enabled reconciliation, AI-based fraud detection, cash flow visibility, AI automation maturity, and operational risk mitigation. Data were analyzed using descriptive statistics, Cronbach’s Alpha reliability testing, Pearson correlation, multiple regression, and hypotheses testing at the 0.05 significance level. The findings showed high to very high agreement that AI automation improves financial control, with operational risk mitigation recording a mean of 4.27, AI-enabled reconciliation 4.31, fraud detection 4.24, invoice processing 4.20, AP automation 4.18, AR automation 4.12, cash flow visibility 4.16, and AI automation maturity 3.98. Reliability was strong, with an overall Cronbach’s Alpha of 0.91. Correlation results confirmed significant positive relationships between all automation variables and operational risk mitigation, with reconciliation showing the strongest association, r = 0.72, p < 0.001, followed by fraud detection, r = 0.70, p < 0.001. Regression analysis showed that the model explained 65.8% of the variance in operational risk mitigation, R² = 0.658, F (7, 212) = 58.47, p < 0.001. AI-enabled reconciliation was the strongest predictor, β = 0.24, followed by fraud detection, β = 0.21. The study implies that AI-driven AP/AR automation should be treated as a strategic financial control mechanism for improving accuracy, transparency, liquidity awareness, fraud prevention, and operational resilience in U.S. SMEs.