X-MHGNN: An Explainable Multimodal Heterogeneous Graph Neural Network for Low-Latency Fraud Mitigation in Streaming Financial Networks

This review looked at how financial fraud detection has changed. It started with machine learning and anomaly detection. Now it uses heterogeneous temporal, explainable and efficiency-aware graph learning. The literature shows useful abilities. Role-aware. Imbalance-aware sampling make graph fraud learning better. Heterogeneous graphs keep entity and relation types. Temporal GNNs show how interactions change over time. Graph transformers give representation and faster operation. Recent studies add counterfactual reasoning to financial fraud detection.   The main observation is that these abilities are still split in the literature. This creates a chance to build a integrated X-MHGNN framework. That framework would mix evidence, heterogeneous temporal graph learning, explainability, imbalance handling, concept-drift adaptation and low-latency streaming inference. I think the proposed framework is a research idea until we test it in experiments. To prove its value we need baselines, component ablations, time-aware tests, explanation checks, robustness trials and full operation metrics.

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