3.1 Systematic Comparative Evaluation Protocols for Financial Anomaly Models
Evaluating intelligent fraud detection frameworks within high-velocity instant payment architectures requires a systematic comparative methodology capable of measuring model robustness across evolving transactional distributions. Instant settlement mechanisms impose strict operational latency constraints that restrict computational complexity during real-time transaction scoring [1]. Consequently, methodological evaluation protocols must assess supervised, unsupervised, and sequential deep learning architectures not only on static predictive accuracy, but also on their computational throughput, inference latency, and resilience against continuous concept drift [5], [8]. The integration of graph analytics introduces structural visibility over coordinated money mule rings and multi-hop fund dispersion, yet demands rigorous benchmarking against the scalability limitations inherent in processing massive transactional graphs under streaming conditions [7]. In this methodological framework, published performance baselines across decision trees, ensemble classifiers, recurrent neural networks, and graph neural networks are comparatively synthesized using standard discrimination criteria, false-positive overhead metrics, and adversarial decay rates [7], [8]. Furthermore, evaluating the efficacy of these analytical models in protecting vulnerable older users requires incorporating longitudinal criteria that measure susceptibility to novel social engineering vectors, where transaction execution appears formally authorized yet reflects deceptive manipulation [5]. By establishing standardized evaluation criteria across algorithmic latency, relational depth, and adversarial adaptability, the methodology provides a robust foundation for assessing how intelligent detection infrastructures maintain long-term digital safety across diverse user demographics without inducing systemic payment friction [1], [5].