Name
Title | ||
|---|---|---|
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach. |
Abstract | ||
|---|---|---|
Tax fraud is the intentional act of lying on a tax return form with intent to lower one's tax liability. Under-reporting is one of the most common types of tax fraud, it consists in filling a tax return form with a lesser tax base. As a result of this act, fiscal revenues are reduced, undermining public investment.
Detecting tax fraud is one of the main priorities of local tax authorities which are required to develop cost-efficient strategies to tackle this problem. Most of the recent works in tax fraud detection are based on supervised machine learning techniques that make use of labeled or audit-assisted data. Regrettably, auditing tax declarations is a slow and costly process, therefore access to labeled historical information is extremely limited. For this reason, the applicability of supervised machine learning techniques for tax fraud detection is severely hindered.
Such limitations motivate the contribution of this work. We present a novel approach for the detection of potential fraudulent tax payers using only unsupervised learning techniques and allowing the future use of supervised learning techniques. We demonstrate the ability of our model to identify under-reporting taxpayers on real tax payment declarations, reducing the number of potential fraudulent tax payers to audit. The obtained results demonstrate that our model doesn't miss on marking declarations as suspicious and labels previously undetected tax declarations as suspicious, increasing the operational efficiency in the tax supervision process without needing historic labeled data.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3219819.3219878 | KDD |
Keywords | Field | DocType |
Unsupervised machine learning,Anomaly detection,Spectral clustering,Kernel density estimation,Tax fraud detection | Revenue,Anomaly detection,Audit,Actuarial science,Computer science,Liability,Supervised learning,Unsupervised learning,Artificial intelligence,Payment,Operational efficiency,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5552-0 | 3 | 0.41 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel de Roux | 1 | 3 | 0.41 |
| Boris Perez | 2 | 10 | 6.67 |
| Andrés Moreno | 3 | 24 | 3.42 |
| Maria Del Pilar Villamil | 4 | 11 | 2.90 |
| César Figueroa | 5 | 3 | 0.41 |