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Link prediction techniques to handle tax evasion

Published: 02 January 2021 Publication History

Abstract

Circular trading of goods is a carefully designed scam ubiquitous among fraudulent business dealers all around the world. Dealers involved in this scheme create an artificial trading network by issuing doctored sales-invoices amongst themselves without any movement of goods. In practice, it is observed that almost all cases of circular trade involve two or three dealers. Here, we work towards predicting circular trade involving three dealers. For the same, we built four different classification models consisting of feature variables tailored for predicting any plausible circular trade amongst three dealers. In particular, the logistic regression model gave the best performance among all the four different models with a prediction accuracy of 80%. Interestingly, we observe that a feature variable formed by using the personalised PageRank technique significantly improves the model over the state of the art link prediction variables. Predicting a future circular trade from a huge network of sales-transactions data is of significant importance to the tax enforcement officers. In addition to automating the process of detecting circular trading, which is manually impossible, this model helps them to target on a set of plausible evaders and take appropriate preventive measures. This model have been developed for the Commercial Taxes Department, Government of Telangana, India, using their first two quarter’s tax returns dataset.

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Cited By

View all
  • (2024)A Survey of Tax Risk Detection Using Data Mining TechniquesEngineering10.1016/j.eng.2023.07.01434(43-59)Online publication date: Mar-2024
  • (2024)Detection of Structured Fraud Supported by Shell Companies on Goods and Services Trading OperationsElectronic Government and the Information Systems Perspective10.1007/978-3-031-68211-7_14(168-183)Online publication date: 15-Aug-2024
  • (2022)Comparative Analysis of Classification Algorithms Applied to Circular Trading Prediction ScenariosElectronic Government and the Information Systems Perspective10.1007/978-3-031-12673-4_7(95-109)Online publication date: 29-Jul-2022

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cover image ACM Other conferences
CODS-COMAD '21: Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD)
January 2021
453 pages
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 January 2021

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Author Tags

  1. PageRank algorithm
  2. circular trading
  3. forensic accounting
  4. goods and services tax
  5. link prediction
  6. logistic regression
  7. tax evasion

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • Visvesvaraya PhD Scheme for Electronics and IT

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CODS COMAD 2021
CODS COMAD 2021: 8th ACM IKDD CODS and 26th COMAD
January 2 - 4, 2021
Bangalore, India

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Overall Acceptance Rate 197 of 680 submissions, 29%

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Cited By

View all
  • (2024)A Survey of Tax Risk Detection Using Data Mining TechniquesEngineering10.1016/j.eng.2023.07.01434(43-59)Online publication date: Mar-2024
  • (2024)Detection of Structured Fraud Supported by Shell Companies on Goods and Services Trading OperationsElectronic Government and the Information Systems Perspective10.1007/978-3-031-68211-7_14(168-183)Online publication date: 15-Aug-2024
  • (2022)Comparative Analysis of Classification Algorithms Applied to Circular Trading Prediction ScenariosElectronic Government and the Information Systems Perspective10.1007/978-3-031-12673-4_7(95-109)Online publication date: 29-Jul-2022

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