Behavioral Drift Analytics for Preventive Fraud Risk Management in Digital Financial Platforms

Abstract

The rapid expansion of digital platforms and online financial ecosystems has substantially increased vulnerabilities to fraudulent activities, account takeovers, and anomalous user behaviors. Traditional risk detection methodologies operate reactively, identi- fying threats only after financial losses or security breaches have materialized. This paper presents the Behavioral Drift Analytics Early Warning System (BDA-EWS), a novel predictive analytics framework specifically architected to detect gradual, cumula- tive changes in user behavior before they culminate in adverse events. The system establishes personalized behavioral baselines through comprehensive statistical profiling of historical transaction data. Unlike conventional anomaly detection approaches that focus on point anomalies, BDA-EWS employs adaptive sliding window analysis with temporal decay to continuously monitor for subtle, evolving shifts in behavioral patterns over time. We introduce a novel multi-component Behavioral Drift Score (BDS) that integrates marginal drift measurements, Mahalanobis distance for multivariate correlation analysis, and Jensen-Shannon di- vergence for distributional change quantification. The framework prioritizes algorithmic transparency through integrated feature contribution analysis, enabling identification of specific behavioral attributes driving detected drift. Extensive evaluation on the enhanced PaySim-2026 dataset comprising 6,362,620 transactions demonstrates that BDA-EWS provides risk alerts 3–7 days before fraudulent transactions with a false positive rate of 8–12%, significantly outperforming point-anomaly methods (25–35% FPR) while maintaining competitive detection accuracy (F1-score: 0.87–0.92). These results establish behavioral drift analytics as an effective paradigm for preventive risk management in contemporary digital financial environments.