FRAUDGUESS: Spotting and Explaining New Types of Fraud in Million-Scale Financial Data

• URL: http://arxiv.org/abs/2509.15493v2
• Date: Sat, 04 Oct 2025 21:31:47 GMT
• Title: FRAUDGUESS: Spotting and Explaining New Types of Fraud in Million-Scale Financial Data
• Authors: Robson L. F. Cordeiro, Meng-Chieh Lee, Christos Faloutsos,
• Abstract summary: We propose FRAUDGUESS to spot new types of fraud and to provide evidence to experts that supports our opinion.<n> FRAUDGUESS is used in real life and is considered for deployment in an Anonymous Financial Institution.<n>We present the three new behaviors that FRAUDGUESS discovered in a real, million-scale financial dataset.
• Score: 16.07352372667229
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given a set of financial transactions (who buys from whom, when, and for how much), as well as prior information from buyers and sellers, how can we find fraudulent transactions? If we have labels for some transactions for known types of fraud, we can build a classifier. However, we also want to find new types of fraud, still unknown to the domain experts ('Detection'). Moreover, we also want to provide evidence to experts that supports our opinion ('Justification'). In this paper, we propose FRAUDGUESS, to achieve two goals: (a) for 'Detection', it spots new types of fraud as micro-clusters in a carefully designed feature space; (b) for 'Justification', it uses visualization and heatmaps for evidence, as well as an interactive dashboard for deep dives. FRAUDGUESS is used in real life and is currently considered for deployment in an Anonymous Financial Institution (AFI). Thus, we also present the three new behaviors that FRAUDGUESS discovered in a real, million-scale financial dataset. Two of these behaviors are deemed fraudulent or suspicious by domain experts, catching hundreds of fraudulent transactions that would otherwise go un-noticed.

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)
• PromoGuardian: Detecting Promotion Abuse Fraud with Multi-Relation Fused Graph Neural Networks [17.818320599254935]
  Promotion abuse is one of the fastest-growing types of fraud in recent years.<n>We conduct the first study on promotion abuse fraud in e-commerce platforms MEITUAN.<n>We introduce PROMOGUARDIAN, a novel multi-relation fused graph neural network that integrates the spatial and temporal information of transaction data into a homogeneous graph to detect promotion abuse fraud.
  arXiv  Detail & Related papers  (2025-10-14T15:48:06Z)
• Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation [28.745101225936697]
  We propose a semi-supervised graph neural network for fraud detection.<n>Specifically, we leverage transaction records to construct a temporal transaction graph.<n>We then pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation.
  arXiv  Detail & Related papers  (2024-12-24T08:48:48Z)
• FRAUDability: Estimating Users' Susceptibility to Financial Fraud Using Adversarial Machine Learning [24.817067681746117]
  We propose FRAUDability, a method for the estimation of a financial fraud detection system's performance for every user. The proposed method produces scores, namely "fraudability scores," which are numerical estimations of a fraud detection system's ability to detect financial fraud for a specific user. We show that the scores can also help attackers increase their financial profit by 54%, by engaging solely with users with high fraudability scores.
  arXiv  Detail & Related papers  (2023-12-02T18:33:05Z)
• 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)
• The Importance of Future Information in Credit Card Fraud Detection [3.2465762663605373]
  We propose a new paradigm: posterior fraud detection with "future" information. On a real-world dataset with over 30 million transactions, it achieves higher performance than a regular LSTM. We believe that future works on this new paradigm will have a significant impact on the detection of compromised cards.
  arXiv  Detail & Related papers  (2022-04-11T17:11:34Z)
• Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection [19.262529179023254]
  We propose a Hierarchical Explainable Network (HEN) to model users' behavior sequences. We also propose a transfer framework to tackle the cross-domain fraud detection problem. Our proposed method is a general transfer framework that could not only be applied upon HEN but also various existing models.
  arXiv  Detail & Related papers  (2022-01-04T06:37:16Z)
• Deep Fraud Detection on Non-attributed Graph [61.636677596161235]
  Graph Neural Networks (GNNs) have shown solid performance on fraud detection. labeled data is scarce in large-scale industrial problems, especially for fraud detection. We propose a novel graph pre-training strategy to leverage more unlabeled data.
  arXiv  Detail & Related papers  (2021-10-04T03:42:09Z)
• Relational Graph Neural Networks for Fraud Detection in a Super-App environment [53.561797148529664]
  We propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App. We use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users. Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity.
  arXiv  Detail & Related papers  (2021-07-29T00:02:06Z)
• Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data [63.942632088208505]
  Super-Apps have changed the way we think about the interactions between users and commerce. This paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior.
  arXiv  Detail & Related papers  (2021-02-19T15:13:06Z)
• DFraud3- Multi-Component Fraud Detection freeof Cold-start [50.779498955162644]
  The Cold-start is a significant problem referring to the failure of a detection system to recognize the authenticity of a new user. In this paper, we model a review system as a Heterogeneous InformationNetwork (HIN) which enables a unique representation to every component. HIN with graph induction helps to address the camouflage issue (fraudsterswith genuine reviews) which has shown to be more severe when it is coupled with cold-start, i.e., new fraudsters with genuine first reviews.
  arXiv  Detail & Related papers  (2020-06-10T08:20:13Z)

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.