Application of AI-Assisted Test Case Generation to Improve Testing Efficiency in Financial Systems

Abstract

Financial systems demand exceptionally high reliability, regulatory compliance, and fault tolerance-attributes that make traditional manual test case generation both time-intensive and prone to human oversight. This study investigates the systematic integration of artificial intelligence--assisted test case generation into the quality assurance pipeline for core financial software, including payment processing engines, real-time risk calculators, and transaction reconciliation modules. A hybrid AI framework was developed, combining symbolic constraint solvers with fine-tuned transformer-based models trained on domain-specific financial logic, regulatory rulebooks, and historical defect patterns. The framework was deployed across three production-grade financial subsystems over a 12-week validation period. Empirical results demonstrate a 47% reduction in average test design cycle time, a 39% increase in edge-case coverage (particularly for multi-currency rounding anomalies, concurrency race conditions under peak load, and regulatory boundary violations), and a 28% improvement in defect detection rate during pre-deployment regression cycles. Crucially, AI-generated test suites achieved 92% alignment with manually authored oracle assertions while reducing false-positive flakiness by 63% compared to conventional fuzzing approaches. The methodology preserves full traceability to functional requirements and supports auditable, explainable test derivation-addressing critical constraints imposed by financial regulators. Findings indicate that AI assistance does not replace human expertise but significantly augments it, shifting tester focus from mechanical case enumeration toward strategic scenario design, oracle validation, and regulatory interpretation.