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New Graph Neural Network Enhances Tax Evasion Detection

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel graph neural network model called TED to enhance tax evasion detection. This model addresses limitations in existing methods by leveraging heterogeneous graphs and related party transaction information, which are crucial in tax scenarios but often overlooked. TED employs a hierarchical attention mechanism to capture deeper structural and semantic insights from these complex transaction groups, aiming to filter out noise and improve detection accuracy. AI

IMPACT This research introduces a novel graph neural network approach that could improve the accuracy and efficiency of detecting tax evasion by analyzing complex transaction relationships.

RANK_REASON The cluster contains a research paper detailing a new model for a specific task.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Graph Neural Network Enhances Tax Evasion Detection

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Yiming Xu, Bin Shi, Bo Dong, Jiaxiang Wang, Hua Wei, Qinghua Zheng ·

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

    arXiv:2605.26984v1 Announce Type: new 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…

  2. arXiv cs.LG TIER_1 English(EN) · Qinghua Zheng ·

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

    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 c…

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