Evaluating the Effectiveness of AI-Based Underwriting Systems in Trade Insurance

Purpose : This study evaluates the effectiveness of artificial intelligence (AI)–based underwriting in trade credit insurance versus traditional manual processes across varied market contexts, focusing on risk-assessment accuracy, operational efficiency, consistency, and cost-effectiveness. Research design, data, methodology : A mixed-methods design analyzes 15,847 applications from eight major providers (developed and emerging markets, 2020–2024). An integrated framework combining the Technology Acceptance Model and the Information Systems Success Model assesses technical, operational, and strategic outcomes using accuracy, processing time, consistency, and cost metrics, with statistical and robustness validation. Results : AI systems improved decision accuracy by 11.2 percentage points (78.4%→89.6%), cut processing time by 61.7% (4.7→1.8 days), and raised daily capacity by 202%. Inter-underwriter agreement rose from 0.643 to 0.924. Direct cost per application fell 62.6%, yielding a 487% three-year ROI with a 6.1-month payback period. All effects were highly significant (p < 0.001) with large effect sizes. Conclusions : AI-based underwriting materially enhances trade credit insurance operations while complementing human expertise. The evidence supports phased implementation, comprehensive change management, and appropriate regulatory frameworks, indicating AI adoption is becoming essential for competitiveness in complex global trade environments.