Applying Benford’s Law to Detect Accounting Data Manipulation in the Banking Industry

Grammatikos, Theoharry; Papanikolaou, Nikolaos I.

doi:10.1007/s10693-020-00334-9

Cited by 28 publications

(26 citation statements)

References 54 publications

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“…The Kolmogorov-Smirnov statistic was applied to detect potential anomalies and incompliance of data in prior research. 12,13 Manipulation is evident if the Kolmogorov-Smirnov statistic is greater than the square root of the total number of the leading digits observed in a probability sample (hereafter referred to as cut-off). Once the K-S statistic is identified, the null hypothesis can be accepted, if: N D n > K n .…”

Section: Methodsmentioning

confidence: 99%

“…Another popular technique is the Pearson chi-square ( χ 2 ) goodness-of-fit test with a confirmatory null hypothesis that the first digit’s distribution must conform to Benford’s frequency curve. 11,12 The chi-square test is sensitive to the sample size and is not recommended for making inferences when the dataset exceeds 5000 observations. 12 The chi-square statistic makes use of the expected number of observations.…”

Section: Methodsmentioning

confidence: 99%

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Can we rely on COVID-19 data? An assessment of data from over 200 countries worldwide

Farhadi

2021

Science Progress

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To fight COVID-19, global access to reliable data is vital. Given the rapid acceleration of new cases and the common sense of global urgency, COVID-19 is subject to thorough measurement on a country-by-country basis. The world is witnessing an increasing demand for reliable data and impactful information on the novel disease. Can we trust the data on the COVID-19 spread worldwide? This study aims to assess the reliability of COVID-19 global data as disclosed by local authorities in 202 countries. It is commonly accepted that the frequency distribution of leading digits of COVID-19 data shall comply with Benford’s law. In this context, the author collected and statistically assessed 106,274 records of daily infections, deaths, and tests around the world. The analysis of worldwide data suggests good agreement between theory and reported incidents. Approximately 69% of countries worldwide show some deviations from Benford’s law. The author found that records of daily infections, deaths, and tests from 28% of countries adhered well to the anticipated frequency of first digits. By contrast, six countries disclosed pandemic data that do not comply with the first-digit law. With over 82 million citizens, Germany publishes the most reliable records on the COVID-19 spread. In contrast, the Islamic Republic of Iran provides by far the most non-compliant data. The author concludes that inconsistencies with Benford’s law might be a strong indicator of artificially fabricated data on the spread of SARS-CoV-2 by local authorities. Partially consistent with prior research, the United States, Germany, France, Australia, Japan, and China reveal data that satisfies Benford’s law. Unification of reporting procedures and policies globally could improve the quality of data and thus the fight against the deadly virus.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Can we rely on COVID-19 data? An assessment of data from over 200 countries worldwide

Farhadi

2021

Science Progress

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show abstract

“…Benfordness can only be applied to data with a geometrical tendency and characterized by the non-existence of minima and maxima. BL is common practice in social sciences and has been applied in various disciplines, such as finance and accounting [12][13][14], politics [15,16], and pandemics [2][3][4][5][6][7][8][9]. The body of knowledge is built on different goodness-of-fit tests to assess the divergence of the observed and expected frequencies.…”

Section: Benford's Law and Goodness-of-fit Testsmentioning

confidence: 99%

Are COVID-19 Data Reliable? A Quantitative Analysis of Pandemic Data from 182 Countries

Farhadi

Lahooti

2021

COVID

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When it comes to COVID-19, access to reliable data is vital. It is crucial for the scientific community to use data reported by independent territories worldwide. This study evaluates the reliability of the pandemic data disclosed by 182 countries worldwide. We collected and assessed conformity of COVID-19 daily infections, deaths, tests, and vaccinations with Benford’s law since the beginning of the coronavirus pandemic. It is commonly accepted that the frequency of leading digits of the pandemic data shall conform to Benford’s law. Our analysis of Benfordness elicits that most countries partially distributed reliable data over the past eighteen months. Notably, the UK, Australia, Spain, Israel, and Germany, followed by 22 different nations, provided the most reliable COVID-19 data within the same period. In contrast, twenty-six nations, including Tajikistan, Belarus, Bangladesh, and Myanmar, published less reliable data on the coronavirus spread. In this context, over 31% of countries worldwide seem to have improved reliability. Our measurement of Benfordness moderately correlates with Johns Hopkin’s Global Health Security Index, suggesting that the quality of data may depend on national healthcare policies and systems. We conclude that economically or politically distressed societies have declined in conformity to the law over time. Our results are particularly relevant for policymakers worldwide.

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“…In accounting, Benford's Law has been used for detecting financial irregularities in accounting data. For instance, it was used to supplement analytical procedures in the area of auditing (Nigrini and Mittermaier, 1997;Drake and Nigrini, 2000), fraud detection (Durtschi, Hillison and Pacini, 2004;Moore and Benjamin, 2004;Cleary and Thibodeau, 2005;Mehta and Bhavani, 2017;Gabrielli and Medioli, 2019), accounting data manipulation (Grammatikos and Papanikolaou, 2021) and to examine taxpayer compliance (Nigrini, 1996). It has also been used to assess the financial reporting quality of entities from a general perspective (e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing Financial Reporting Quality of Company Limited by Guarantee Charities in Malaysia Using Benford’s Law: A Preliminary Study

Cheuk¹,

Nichol²,

Daw³

et al. 2021

IJARAFMS

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The issue of terrorism is experienced at the global level, and it has been recognised that such activities need financing from various sources, legitimate and illegitimate. A legitimate source is via non-profit organisations. It has been advocated that effective investigation and information gathering be done at the country-level to facilitate the detection of terrorist abuse of non-profit organisations; such information would include financial information. In view of the foregoing, a comprehensive assessment of the financial reporting quality of non-profit organisations would be a useful step in conducting an overall risk analysis of the non-profit sector. The objective of this study is to assess the financial reporting quality of charities in Malaysia. The financial statements of 235 companies limited by guarantee were extracted for the years 2016, 2017 and 2018 resulting in 7,056 observations. Digital analysis was performed to review the conformity of first and second digit distributions with that of Benford's Law; the main statistics of the log mantissa and the Mean Absolute Deviation were also computed. Subsequently, statistical tests were run to evaluate data conformity to Benford's Law. Findings were found to be mixed and inconclusive. Implications of the study's findings and suggestions for further research were further discussed.

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Applying Benford’s Law to Detect Accounting Data Manipulation in the Banking Industry

Cited by 28 publications

References 54 publications

Can we rely on COVID-19 data? An assessment of data from over 200 countries worldwide

Can we rely on COVID-19 data? An assessment of data from over 200 countries worldwide

Are COVID-19 Data Reliable? A Quantitative Analysis of Pandemic Data from 182 Countries

Assessing Financial Reporting Quality of Company Limited by Guarantee Charities in Malaysia Using Benford’s Law: A Preliminary Study

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