Big Data Analytics for Tax Administration

Mehta, Priya; Mathews, Jithin; Kumar, Kapil; Suryamukhi, K.; Babu, Ch. Sobhan; Rao, S. V. Kasi Visweswara; Shivapujimath, Vishal; Bisht, Dikshant

doi:10.1007/978-3-030-27523-5_4

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…In this regard, Mehta et al (2019) present tax evasion by means of massive data analytics, Android Apps, and IT. This approach identifies suspicious dealers utilizing specific parameters and normalizes the process of examination of accounts.…”

Section: The Measurement Of Tax Fraudmentioning

confidence: 99%

A proposal of a suspicion of tax fraud indicator based on Google trends to foresee Spanish tax revenues

Monge

Poza

Borgia

2022

International Economics

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This article contributes to the relationship between fiscal fraud and tax collection in the Spanish economy, creating a composite suspicion tax fraud indicator (STFI) based on Google Trends searches to study the dynamics and foresee tax revenues evolution in Spain. Also, we expand knowledge in the field of fraud tax indicators, following the UNODC (2020) and OECD (2016) recommendations. To this purpose, we apply factor analysis to create the composite indicator and next we utilize techniques centred on fractional integration (ARFIMA) and fractional cointegration VAR (FCVAR) to assess the STFI behavior against tax collection and GDP. The outcomes indicate that the d is less than 1 in all the time series analyzed. As we can see, the tax collection and the leading indicator have a similar statistical behavior (d=0.49 and d=0.40, respectively), which implies mean reversion. On the other hand, GDP will behave similarly to the other two time series, with d = 0.05, which implies that the shocks will have a temporary effect on the GDP behavior, and these effects will disappear by themselves in the short term and in less time than the other two time series. FCVAR results indicate a short-lived duration of the shock due to the error correction term and their short-run stationary behaviour. In the end, applying wavelet analysis, we determine that the composite suspicion tax fraud indicator maintains a negative association with tax collection, except in 2017 and 2018, when the high economic growth offsets the fiscal fraud.

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Section: The Measurement Of Tax Fraudmentioning

confidence: 99%