An Adaptive Fraud Risk Scoring Model for Real-Time Transaction Monitoring at Scale
Abstract
The rapid growth of digital payment platforms, mobile banking, and e-commerce ecosystems has significantly increased transaction volumes and velocity, creating fertile ground for sophisticated and large-scale financial fraud. Traditional rule-based fraud detection systems and static machine learning models have struggled to cope with the evolving nature of fraud patterns, the scale of real-time transaction streams, and the need for low-latency decision-making. This paper proposes an adaptive fraud risk scoring model designed for real-time transaction monitoring at scale. The model integrates incremental learning, ensemble-based risk scoring, and concept drift awareness to continuously adjust to emerging fraud behaviors while maintaining operational stability. Rather than introducing novel algorithms, the study synthesizes and operationalizes established techniques including supervised classification, anomaly detection, and streaming analytics, within a unified adaptive framework suitable for high-throughput financial systems. A comprehensive review of the literature highlights limitations of static models, delayed retraining cycles, and scalability bottlenecks in existing approaches. The proposed architecture emphasizes explainability, computational efficiency, and regulatory compatibility, making it applicable to large financial institutions and payment service providers. The paper contributes by offering a system-level perspective on adaptive fraud risk scoring, bridging the gap between theoretical models and real-world deployment requirements for real-time fraud prevention.
Keywords
Fraud detection; Risk scoring; Real-time transaction monitoring; Adaptive models; Financial crime analytics; Streaming data
References
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