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Business and Social Sciences

Volume 1 Number 1 2023

Article Contents

RESEARCH ARTICLE   (Open Access)
Contents Vol 1 (1)

Detecting Tax Evasion and Financial Crimes in The United States Using Advanced Data Mining Technique

Md Rakibul Haque Pranto1*, Ismoth Zerine2, Md Mainul Islam2, Morium Akter3, Tauhedur Rahman4

+ Author Affiliations

Business and Social Sciences 1 (1) 1-11 https://doi.org/10.25163/business.1110337

Submitted: 01 January 2023 Revised: 05 March 2023  Published: 07 March 2023 

Abstract

Background: Financial crimes and tax evasion pose a major threat to the financial security and integrity of the United States. While regulatory mechanisms exist, detecting fraudulent activity within massive and complex financial datasets remains a significant challenge. Advanced data mining techniques offer the potential to improve the identification of irregularities and strengthen oversight systems. Methods: This study applied advanced data mining methods—including decision trees, neural networks, logistic regression, and K-means clustering—to identify patterns of financial crime and tax evasion. The dataset consisted of one million financial records obtained from publicly available IRS and SEC reports between 2015 and 2020. Stratified random sampling ensured broad representation across industries. Analytical approaches included logistic regression, K-means clustering, and correlation analysis, with performance evaluated through p-values, sensitivity, specificity, and area under the ROC curve (AUC). Results: The analysis showed that lower tax payments and higher anomaly scores were strong indicators of tax evasion. A significant positive correlation was observed between transaction volume and anomaly score (r = 0.52, p < 0.0001). Logistic regression identified tax paid, income, transaction volume, and anomaly score as significant predictors of tax evasion, achieving excellent classification accuracy with an AUC of 0.91. K-means clustering further classified entities into three risk categories, with the high-risk cluster exhibiting substantially higher values for income, transaction volume, and anomaly scores compared to other groups. Conclusion: The findings demonstrate the effectiveness of advanced data mining techniques for detecting tax evasion and financial crimes in large-scale datasets. By identifying key predictive indicators and stratifying risk, these methods show strong potential for integration into financial oversight systems. Their implementation could significantly enhance the capacity of regulatory frameworks to detect, prevent, and mitigate financial misconduct in real time.

Keywords: Tax evasion, financial crimes, data mining, logistic regression, anomaly detection.

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