Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook

Abstract

In recent years, the unprecedented growth in digital payments fueled consequential changes in fraud and financial crimes. In this new landscape, traditional fraud detection approaches such as rule-based engines have largely become ineffective. AI and machine learning solutions using graph computing principles have gained significant interest. Graph neural networks and emerging adaptive solutions provide compelling opportunities for the future of fraud and financial crime detection. However, implementing the graph-based solutions in financial transaction processing systems has brought numerous obstacles and application considerations to light. In this paper, we overview the latest trends in the financial crimes landscape and discuss the implementation difficulties current and emerging graph solutions face. We argue that the application demands and implementation challenges provide key insights in developing effective solutions.

References in corpus (14)

• Playing Atari with Deep Reinforcement Learning
• Graph Neural Networks: A Review of Methods and Applications
• Adversarial Attacks on Neural Networks for Graph Data
• Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
• A Comprehensive Survey of Data Mining-based Fraud Detection Research
• Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
• Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case
• Transferring Robustness for Graph Neural Network Against Poisoning Attacks
• Machine Learning on Graphs: A Model and Comprehensive Taxonomy
• Temporal Graph Networks for Deep Learning on Dynamic Graphs
• Scalable Graph Learning for Anti-Money Laundering: A First Look
• Explainability Techniques for Graph Convolutional Networks
• Deep Learning in Finance
• Streaming Graph Neural Networks