Аңдатпа
Digital salary advance and earned wage access platforms require intelligent monitoring mechanisms capable of identifying behavioral anomalies, estimating operational risk, and preserving auditable transaction evidence. Existing financial anomaly detection approaches commonly rely on isolated predictive models, static anomaly thresholds, or rule-based monitoring, providing limited support for behavior-aware enterprise monitoring. This study proposes a behavioral drift-aware adaptive anomaly fusion framework for explainable risk monitoring in salary advance FinTech systems. The framework introduces the Behavioral Risk Deviation Index (BRDI), which integrates temporal irregularity, behavioral entropy, transaction velocity, burst activity, and historical behavioral drift into a unified behavioral state representation. BRDI adaptively regulates the fusion of Isolation Forest-based statistical anomaly scores and reconstruction-based nonlinear anomaly scores according to each employee’s behavioral profile. The framework further combines calibrated ensemble risk scoring, SHAP-based explainability, and blockchain-assisted audit verification using SHA-256 hashing and Merkle-root validation. Experimental evaluation on 52,846 anonymized salary advance transactions demonstrates that the framework supports behavior-aware anomaly detection, interpretable risk prioritization, and trustworthy audit traceability in enterprise financial environments. The proposed approach contributes a unified behavioral state representation, a deterministic adaptive anomaly fusion mechanism, and an integrated architecture for trustworthy AI-assisted salary advance monitoring.