A Multi-Layered Graph based Real-Time Transaction Streaming and Governance Framework
Another challenge that is still among the most critical issues to address in fraud detection is the financial transaction fraud detection in the light of the dynamic, adaptive, and relational nature of the fraud situation. Conventional rule-based systems are interpretable, but not flexible, whereas purely data-driven machine learning methods tend to lack a sparse labeling issue as well as explainability. The paper presents a hybrid scheme of detecting fraud which involves rule-based risk assessment and unsupervised machine learning through graphs to detect anomalous behavior in transactions in real time. The first step in processing transactional data is to sessionize the data and then represent the data as a heterogeneous interaction graph, reflecting the relationship between users, devices, IP addresses and products. Learning of graph representation This is done by learning higher-order structural and behavioral patterns by learning the random-walk-based representation of the nodes. The learned embedding space is then used to perform anomaly detection with the aid of Isolation Forest to identify suspicious entities whose relational behavior is not normal. Risk evaluation is then done simultaneously by a rule-processing module that implements predetermined domain constraints like transaction value limits, geographic deviations and IP-based risk indicators. All the activated rules add to a cumulative risk score, which gives traceable rationale behind the assessment. The general fraud categorization is achieved by combining the anomaly score of the graph-based model and the combined rule-based risk score. It is a more reliable mechanism of detection and is compatible with the interpretability needed to operate and make regulations in practice, and this experimental 93% accuracy gives us evidence that the system can detect known and never-seen patterns in frauds, a scalable, explainable and practical answer to real-world financial fraud detection.