Validating Benfordness on contaminated data

Marco Di Marzio, Stefania Fensore and Chiara Passamonti

Socio-Economic Planning Sciences, 2024, vol. 95, issue C

Abstract: Benford’s law is a mathematical model, very recurrent in practice for a wide variety of datasets, used to represent the frequencies of digits. A well-established usage of Benfordness statistical testing lies within investigations aimed to ascertain if balance sheet and income statement data are genuine. A typical, frustrating problem of Benfordness statistical tests on big, practical datasets is that they often provide p-valuessmaller than expected when the Benfordness null hypothesis is very realistic. A possible reason is that data are contaminated by some kind of noise. In this paper we propose the deconvolution approach to alleviate this issue, using both simulated and real data.

Keywords: Benford; Deconvolution method; Fraud detection; Kernel density estimation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:soceps:v:95:y:2024:i:c:s0038012124002076

DOI: 10.1016/j.seps.2024.102008

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