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- W4360921200 abstract "The intentional alteration of a tax return form with the intent to reduce one’s tax base is known as tax fraud. Underreporting, which entails filing a tax return with a lower tax base by either raising purchases or lowering sales, is one of the most widespread types of tax fraud. Such an action weakens government spending by reducing government revenues. Since there are not enough resources or auditors to handle the situation, tax authorities must come up with low-cost solutions. Therefore, one of their top priorities should be identifying tax fraud. The vast bulk of research on tax fraud detection is based on supervised machine learning techniques that make use of the findings of tax return audits. Unfortunately, access to audited and labeled tax returns is quite restricted because it is an expensive and time-consuming process. This places severe restrictions on supervised machine learning techniques. The work in this paper focuses on finding solutions to these constraints. We specifically outline our method for finding anomalies in tax returns using stacked autoencoders (SAEs), along with a probability distribution of the suspicious values for each field on the tax return form. By comparing the outcomes of our method with two existing anomaly techniques that have been utilized in the literature, we show how well our model can identify current tax fraud schemes." @default.
- W4360921200 created "2023-03-26" @default.
- W4360921200 creator A5050131505 @default.
- W4360921200 date "2023-01-01" @default.
- W4360921200 modified "2023-10-05" @default.
- W4360921200 title "Value-Added Tax Fraud Detection and Anomaly Feature Selection Using Sectorial Autoencoders" @default.
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- W4360921200 doi "https://doi.org/10.1007/978-981-19-7615-5_29" @default.
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