Predictability of financial statements fraud-risk using Benford’s Law

Rad, Mehdi Samee; Amiri, Ali; Ranjbar, Mohammad; Salari, Hojatollah

doi:10.1080/23322039.2021.1889756

Cited by 7 publications

(5 citation statements)

References 15 publications

(11 reference statements)

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“…The greatest contribution to the development of Benford's Law was made by Professor Mark Nigrini, who proposed a methodology for the integrated application of Benford's Law tests to detect financial fraud (Nigrini, 2012;Nigrini, 1993). Validation of the tests proposed by Mark Nigrini on different sets of corporate data proves their effectiveness in terms of identifying anomalies in large sample populations (Pupokusumo et al, 2022;Leonov et al, 2021;Rad et al, 2021;Manuel and Garcia, 2021). The adaptable nature of Benford's law allows it to be combined with other statistical approaches, which has been done in previous research with neural networks, logistic regression, decision trees, and random forests (Bhosale and Troia, 2022;Badal-Valero, Alvarez-Jareño, and Pavía, 2018;Bhattacharya, Xu and Kumar, 2011).…”

Section: Literature Reviewmentioning

confidence: 96%

Integrating Data Mining Techniques for Fraud Detection in Financial Control Processes

Sushkov,

Leonov,

Nadezhina

et al. 2023

IJTech

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Detecting fraud in financial control processes poses significant challenges due to the complex nature of financial transactions and the evolving tactics employed by fraudsters. This paper investigates the integration of data mining techniques, specifically the combination of Benford's Law and machine learning algorithms, to create an enhanced framework for fraud detection. The paper highlights the importance of combating fraudulent activities and the potential of data mining techniques to bolster detection efforts. The literature review explores existing methodologies and their limitations, emphasizing the suitability of Benford's Law for fraud detection. However, shortcomings in practical implementation necessitate improvements for its effective utilization in financial control. Consequently, the article proposes a methodology that combines informative statistical features revealed by Benford's law tests and subsequent clustering to overcome its limitations. The results present findings from a financial audit conducted on a road-construction company, showcasing representations of primary, advanced, and associated Benford's law tests. Additionally, by applying clustering techniques, a distinct class of suspicious transactions is successfully identified, highlighting the efficacy of the integrated approach. This class represents only a small proportion of the entire sample, thereby significantly reducing the labor costs of specialists for manual audit of transactions. In conclusion, this paper underscores the comprehensive understanding that can be achieved through the integration of Benford's Law and other data mining techniques in fraud detection, emphasizing their potential to automate and scale fraud detection efforts in financial control processes.

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Section: Literature Reviewmentioning

confidence: 96%

Integrating Data Mining Techniques for Fraud Detection in Financial Control Processes

Sushkov,

Leonov,

Nadezhina

et al. 2023

IJTech

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“…(2012), Nigrini (2017) and Rad et al. (2021). Notably, Benford's law is not without its limitations and questions have been raised about its propensity to generate too many false negatives when used for fraud detection.…”

Section: Bringing the S Factor Into Play – An Illustrative Model Usin...mentioning

confidence: 99%

“…Benford's law has been too widely applied in the context of fraud to be meaningfully reviewed here; interested readers are directed towards readings such as Bhattacharya et al (2011), Stambaugh et al (2012, Nigrini (2017) and Rad et al (2021). Notably, Benford's law is not without its limitations and questions have been raised about its propensity to generate too many false negatives when used for fraud detection.…”

Section: Br I Ngi Ng T H E S Factor I N To Pl Ay -A N I L Lust R At I...mentioning

confidence: 99%

Taking the hunch out of the crunch: A framework to improve variable selection in models to detect financial statement fraud

Gepp,

Kumar,

Bhattacharya

2023

Accounting & Finance

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Financial statement fraud is a costly problem for society. Detection models can help, but a framework to guide variable selection for such models is lacking. A novel Fraud Detection Triangle (FDT) framework is proposed specifically for this purpose. Extending the well‐known Fraud Triangle, the FDT framework can facilitate improved detection models. Using Benford's law, we demonstrate the posited framework's utility in aiding variable selection via the element of surprise evoked by suspicious information latent in the data. We call for more research into variables that measure rationalisations for fraud and suspicious phenomena arising as unintended consequences of financial statement fraud.

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“…The literature has several methods for estimating the probability of accounting manipulation (e.g., ratio analysis, Beneish model, Benford's law, data mining, and others) (Zack, 2013;Kliestik et al, 2022;Mantone, 2013;Tutino and Merlo, 2019;Gruszczyński;2020;Rad et al, 2021;Isaković-Kaplan et al, 2021).…”

Section: Beneish Modelmentioning

confidence: 99%

Investigation of accounting manipulation using the Beneish model: Hungarian case

Fenyves,

Pisula,

Tarnoczi

2023

Economics &Amp; Sociology

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The study examined the manipulation level of Hungarian corporate financial statements using Beneish’s M-score model with eight variables between 2017 and 2021. The research also investigated whether the financial statement manipulations depend on the type of sector, company size and age, and region. The research sample was comprised of 32,024 financial statements each year. Statistical tests were used to compare the M-score values of several groups. The proportion of companies with possibly manipulated financial statements varied between 46.43% and 51.67% in the five years. It can be concluded that the manipulation of Hungarian companies’ reports is very high. The analysis showed that the ratios of unlikely manipulated (UM) and likely manipulated (LM) reports were improved at size category 1-4, and size category five significantly improved. The comparison by regions revealed that the UM/LM indicator is lower in more developed regions than in less developed ones. The results draw the attention of government decision-makers to pay more attention to checking financial statements. In addition, it shows to the companies’ stakeholders that the reliability of the financial statements must also be considered during their decision preparations and risk assessment.

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Predictability of financial statements fraud-risk using Benford’s Law

Cited by 7 publications

References 15 publications

Integrating Data Mining Techniques for Fraud Detection in Financial Control Processes

Integrating Data Mining Techniques for Fraud Detection in Financial Control Processes

Taking the hunch out of the crunch: A framework to improve variable selection in models to detect financial statement fraud

Investigation of accounting manipulation using the Beneish model: Hungarian case

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