Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph

• URL: http://arxiv.org/abs/2308.02793v2
• Date: Thu, 4 Apr 2024 05:10:06 GMT
• Title: Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph
• Authors: Zequan Xu, Qihang Sun, Shaofeng Hu, Jieming Shi, Hui Li,
• Abstract summary: We propose a novel contrastive multi-view learning method named CMT for crowdsourcing fraud detection over the heterogeneous temporal graph (HTG) of MMMA. We deploy CMT to detect crowdsourcing frauds on an industry-size HTG of a representative MMMA WeChat and it significantly outperforms other methods.
• Score: 5.448839082856454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rise of the click farm business using Multi-purpose Messaging Mobile Apps (MMMAs) tempts cybercriminals to perpetrate crowdsourcing frauds that cause financial losses to click farm workers. In this paper, we propose a novel contrastive multi-view learning method named CMT for crowdsourcing fraud detection over the heterogeneous temporal graph (HTG) of MMMA. CMT captures both heterogeneity and dynamics of HTG and generates high-quality representations for crowdsourcing fraud detection in a self-supervised manner. We deploy CMT to detect crowdsourcing frauds on an industry-size HTG of a representative MMMA WeChat and it significantly outperforms other methods. CMT also shows promising results for fraud detection on a large-scale public financial HTG, indicating that it can be applied in other graph anomaly detection tasks. We provide our implementation at https://github.com/KDEGroup/CMT.

Related papers

• When AI Agents Collude Online: Financial Fraud Risks by Collaborative LLM Agents on Social Platforms [101.2197679948061]
  We study the risks of collective financial fraud in large-scale multi-agent systems powered by large language model (LLM) agents.<n>We present MultiAgentFraudBench, a large-scale benchmark for simulating financial fraud scenarios.
  arXiv  Detail & Related papers  (2025-11-09T16:30:44Z)
• Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions [51.43521977132062]
  Money laundering is a financial crime that obscures the origin of illicit funds. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated anti-money laundering investigations. This paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML.
  arXiv  Detail & Related papers  (2025-03-13T05:19:44Z)
• A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection [60.09453163562244]
  We propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD. In the estimation module, we design a novel label-free heterophily metric called HALO, which captures the critical graph properties for GFD. In the alignment-based fraud detection module, we develop a joint-GNN architecture with ranking loss and asymmetric alignment loss.
  arXiv  Detail & Related papers  (2025-02-18T22:07:36Z)
• Heterogeneous Graph Auto-Encoder for CreditCard Fraud Detection [0.7864304771129751]
  This paper proposes a novel approach for credit card fraud detection using Graph Neural Networks (GNNs) with attention mechanisms applied to heterogeneous graph representations of financial data. The proposed model outperforms benchmark algorithms such as Graph Sage and FI-GRL, achieving a superior AUC-PR of 0.89 and an F1-score of 0.81.
  arXiv  Detail & Related papers  (2024-10-10T17:05:27Z)
• Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Search [2.4374097382908477]
  We introduce a state-of-the-art hybrid ensemble (ENS) Machine learning (ML) model that intelligently combines multiple algorithms to enhance fraud identification. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, and ENS models, respectively.
  arXiv  Detail & Related papers  (2024-02-22T09:01:42Z)
• Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation [49.827306773992376]
  Continual Test-Time Adaptation (CTTA) is proposed to migrate a source pre-trained model to continually changing target distributions. Our proposed method attains state-of-the-art performance in both classification and segmentation CTTA tasks.
  arXiv  Detail & Related papers  (2023-12-19T15:34:52Z)
• Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market [0.0]
  This research article aims to evaluate the efficacy of a deep semi-supervised anomaly detection technique, called Deep SAD, for detecting fraud in high-frequency financial data. We use exclusive proprietary limit order book data from the TMX exchange in Montr'eal, with a small set of true labeled instances of fraud, to evaluate Deep SAD. We show that incorporating a small amount of labeled data into an unsupervised anomaly detection framework can greatly improve its accuracy.
  arXiv  Detail & Related papers  (2023-08-31T19:07:50Z)
• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• MGTBench: Benchmarking Machine-Generated Text Detection [54.81446366272403]
  This paper proposes the first benchmark framework for MGT detection against powerful large language models (LLMs) We show that a larger number of words in general leads to better performance and most detection methods can achieve similar performance with much fewer training samples. Our findings indicate that the model-based detection methods still perform well in the text attribution task.
  arXiv  Detail & Related papers  (2023-03-26T21:12:36Z)
• Self-supervised Graph Representation Learning for Black Market Account Detection [62.03978210281426]
  Black market accounts (BMAs) are not directly involved in frauds and are more difficult to detect. This paper illustrates our BMA detection system SGRL (Self-supervised Graph Learning) used in WeChat, a representative MMMA with over a billion users. We deploy SGRL in the online environment to detect BMAs on the billion-scale WeChat graph, and it exceeds the alternative by 7.27% on the online evaluation measure.
  arXiv  Detail & Related papers  (2022-12-06T00:42:00Z)
• BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition [57.2201011783393]
  conditional hidden Markov model (CHMM) CHMM predicts token-wise transition and emission probabilities from the BERT embeddings of the input tokens. It fine-tunes a BERT-based NER model with the labels inferred by CHMM.
  arXiv  Detail & Related papers  (2021-05-26T21:18:48Z)
• Applying support vector data description for fraud detection [0.0]
  One of the main challenges in fraud detection is acquiring fraud samples which is a complex and challenging task. In order to deal with this challenge, we apply one-class classification methods such as SVDD which does not need the fraud samples for training. Also, we present our algorithm REDBSCAN which is an extension of DBSCAN to reduce the number of samples and select those that keep the shape of data.
  arXiv  Detail & Related papers  (2020-05-31T21:31:32Z)
• Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation [93.83369981759996]
  We propose a self-supervised equivariant attention mechanism (SEAM) to discover additional supervision and narrow the gap. Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation. We propose consistency regularization on predicted CAMs from various transformed images to provide self-supervision for network learning.
  arXiv  Detail & Related papers  (2020-04-09T14:57:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.