AI-Powered Fraud Detection in Financial Services: GNN, Compliance Challenges, and Risk Mitigation
This paper introduces a GNN-based framework for accurate, scalable, and explainable financial fraud detection and compliance enhancement.
What it examines
This study applies Graph Neural Networks to financial fraud detection by modeling transactions as graphs to capture complex relationships. It addresses limitations of rule-based and traditional machine learning models, aiming to improve fraud detection accuracy, scalability, and regulatory compliance using deep learning and explainable AI techniques.
What it concludes
The study shows GNNs significantly improve fraud detection by leveraging transaction relationships. Results indicate higher accuracy and recall compared to traditional models. Potential applications include fraud prevention in banking, AML/KYC compliance, and risk management. Future research should refine scalability, explainability, and real-time processing for broader financial use.
Evidence objects
Graph Neural Networks, notably Graph Attention Networks, revolutionize fraud detection by capturing hidden transactional relationships and achieving a 91% recall compared to conventional models 72% accuracy in identifying fraudulent schemes.
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The research outlines a framework integrating network science, deep learning, and Explainable AI, introducing novel graph modeling of transactional data with crucial indicators like high volumes and node degree metrics.
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Experimental results demonstrate that GNNs operate efficiently on real-world datasets using mini-batch training and sampling methods, despite challenges regarding computational overhead and diverse data requirements to ensure deployment scalability effectively.
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The paper innovatively combines $GNN$ and $\text{XAI}$, delivering a pioneering solution for fraud detection in finance. It captures intricate relational dependencies and real-time anomalies while addressing scalability and regulatory transparency. This novel integration offers valuable insights, promising compelling advancements in quantitative risk management and compliance across diverse financial applications remarkably.
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Raw abstract and provenance
- … Furthermore, we evaluate computational efficiency and real-time feasibility, … highfrequency financial environments. Our findings suggest that integrating GNNs into financial …
Source row: 143 · abstract type: snippet