mlsanityData to 7 Oct 2026

Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks

arXiv 2409.09892 ↗First posted 15 Sep 2024cs.LG cs.SIcited by 19

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Abstract (arXiv)

Financial fraud refers to the act of obtaining financial benefits through dishonest means. Such behavior not only disrupts the order of the financial market but also harms economic and social development and breeds other illegal and criminal activities. With the popularization of the internet and online payment methods, many fraudulent activities and money laundering behaviors in life have shifted from offline to online, posing a great challenge to regulatory authorities. How to efficiently detect these financial fraud activities has become an urgent issue that needs to be resolved. Graph neural networks are a type of deep learning model that can utilize the interactive relationships within graph structures, and they have been widely applied in the field of fraud detection. However, there are still some issues. First, fraudulent activities only account for a very small part of transaction transfers, leading to an inevitable problem of label imbalance in fraud detection. At the same time, fraudsters often disguise their behavior, which can have a negative impact on the final prediction results. In addition, existing research has overlooked the importance of balancing neighbor information and central node information. For example, when the central node has too many neighbors, the features of the central node itself are often neglected. Finally, fraud activities and patterns are constantly changing over time, so considering the dynamic evolution of graph edge relationships is also very important.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 10 of its 26 references that are in the map. First posted on arXiv 15 Sep 2024.

Builds on

10 of 26 references.

References · 10

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 248
  4. 2024Reinforcement learningcited by 236
  5. 2020Heterogeneous Graph Neural Networks for Malicious Account Detectioncited by 95
  6. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  7. 2024Advanced Financial Fraud Detection Using GNN-CL Modelcited by 11
  8. 2023Optimizing Distributed Networking with Big Data Scheduling and Cloud Computingcited by 10
  9. 2024Portfolio Optimization with Robust Covariance and Conditional Value-at-Risk Constraintscited by 9
  10. 2024GraphCNNpred: A stock market indices prediction using a Graph based deep learning systemcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 14; 2025: 4; 2026: 1.

19 papers.

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  1. 2024Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networkscited by 33
  2. 2024Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discoverycited by 19
  3. 2024Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layoutcited by 14
  4. 2024Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learningcited by 13
  5. 2024Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issuescited by 12
  6. 2024Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessmentcited by 11
  7. 2024Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classificationcited by 11
  8. 2024Enhanced Recommendation Combining Collaborative Filtering and Large Language Modelscited by 10
  9. 2025Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacycited by 9
  10. 2025User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Datacited by 8
  11. 2024Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processingcited by 7
  12. 2024In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before An Ongoing Trajectory Terminatescited by 7
7 more
  1. 2024Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Managementcited by 6
  2. 2024Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systemscited by 5
  3. 2024A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithmcited by 5
  4. 2024Real-time Video Target Tracking Algorithm Utilizing Convolutional Neural Networks (CNN)cited by 4
  5. 2025Generating Multimodal Images with GAN: Integrating Text, Image, and Stylecited by 0
  6. 2025Quantum Topological Graph Neural Networks for Detecting Complex Fraud Patternscited by 0
  7. 2026Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Modelscited by 0