TED: Related Party Transaction guided Tax Evasion Detection on Heterogeneous Graph

• URL: http://arxiv.org/abs/2605.26984v1
• Date: Tue, 26 May 2026 13:09:37 GMT
• Title: TED: Related Party Transaction guided Tax Evasion Detection on Heterogeneous Graph
• Abstract summary: We propose a novel graph neural network model to improve the performance of tax evasion detection.<n>Specifically, we use heterogeneous and complex related party transaction groups to filter low-level noise information.<n>We apply our method to the real risk management system of the tax bureau, and evaluate it on two human-labeled real-world tax datasets.
• Score: 41.897301755940454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tax evasion causes severe losses of government revenues and disturbs the economic order of fair competition. To help alleviate this problem, the latest tax evasion detection solutions utilize expert knowledge to extract features and then train classifiers to determine whether a company is suspected of tax evasion. However, existing solutions mainly focus on the statistical features of the company, but fail to exploit the rich interactive information in tax scenarios, which affect the detection performance. In this paper, we first model the tax scenario as a heterogeneous graph and study the tax evasion detection problem under the heterogeneous graph model. To improve the performance of tax evasion detection, a novel graph neural network model is proposed to extract the comprehensive information of heterogeneous graphs. Specifically, we use heterogeneous and complex related party transaction groups to filter low-level noise information. Moreover, a hierarchical attention mechanism is designed to capture the deeper structure and semantic information hidden in the related party transaction group. We apply our method to the real risk management system of the tax bureau, and evaluate it on two human-labeled real-world tax datasets. The results demonstrate that our method significantly outperforms the state-of-the-art in the tax evasion detection task.

Related papers

• 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.<n>In the estimation module, we design a novel label-free heterophily metric called HALO, which captures the critical graph properties for GFD.<n>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)
• Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations [63.52709761339949]
  We first contribute a dedicated dataset called the Fair Forgery Detection (FairFD) dataset, where we prove the racial bias of public state-of-the-art (SOTA) methods.<n>We design novel metrics including Approach Averaged Metric and Utility Regularized Metric, which can avoid deceptive results.<n>We also present an effective and robust post-processing technique, Bias Pruning with Fair Activations (BPFA), which improves fairness without requiring retraining or weight updates.
  arXiv  Detail & Related papers  (2024-07-19T14:53:18Z)
• A Taxation Perspective for Fair Re-ranking [61.946428892727795]
  We introduce a new fair re-ranking method named Tax-rank, which levies taxes based on the difference in utility between two items. Our model Tax-rank offers a superior tax policy for fair re-ranking, theoretically demonstrating both continuity and controllability over accuracy loss.
  arXiv  Detail & Related papers  (2024-04-27T08:21:29Z)
• On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software [12.071874385139395]
  Nearly 50% of taxpayers filed their individual income taxes using tax software in the U.S. in FY22. This paper formulates the task of generating metamorphic specifications as a translation task between properties extracted from tax documents.
  arXiv  Detail & Related papers  (2023-11-20T18:12:28Z)
• Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks [62.94317686301643]
  Company financial risk is ubiquitous and early risk assessment for listed companies can avoid considerable losses. Traditional methods mainly focus on the financial statements of companies and lack the complex relationships among them. We propose a novel Hierarchical Graph Neural Network (TH-GNN) for Tribe-style graphs via two levels, with the first level to encode the structure pattern of the tribes with contrastive learning, and the second level to diffuse information based on the inter-tribe relations.
  arXiv  Detail & Related papers  (2023-01-31T09:17:13Z)
• The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection [17.254383007779616]
  We argue that the performance degradation is mainly attributed to the inconsistency between topology and attribute. We propose a simple and effective method that uses the attention mechanism to adaptively fuse two views. Our model can significantly outperform stateof-the-art baselines on real-world fraud detection datasets.
  arXiv  Detail & Related papers  (2022-10-22T08:21:49Z)
• Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models [73.24381010980606]
  This study examines issues of algorithmic fairness in the context of systems that inform tax audit selection by the IRS. We show how the use of more flexible machine learning methods for selecting audits may affect vertical equity. Our results have implications for the design of algorithmic tools across the public sector.
  arXiv  Detail & Related papers  (2022-06-20T16:27:06Z)
• An Evolutionary Game Model for Understanding Fraud in Consumption Taxes [0.0]
  This paper presents a computational evolutionary game model to study and understand fraud dynamics in the consumption tax system. Players are cooperators if they correctly declare their value added tax (VAT), and are defectors otherwise. Since transactions between companies must be declared by both the buyer and seller, a strategy adopted by one influences the other's payoff.
  arXiv  Detail & Related papers  (2021-01-12T11:53:31Z)

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.