scholarly journals Benford’s Law as an Instrument for Fraud Detection in Surveys Using the Data of the Socio-Economic Panel (SOEP)

2011 ◽  
Vol 231 (5-6) ◽  
Author(s):  
Jörg-Peter Schräpler
Keyword(s):  
Probability Function ◽  
Fraud Detection ◽  
Numerical Data ◽  
Benford’S Law ◽  
Continuous Data ◽  
Data Set ◽  
Benford's Law ◽  
User Data

SummaryThis paper focuses on fraud detection in surveys using Socio-Economic Panel (SOEP) data as an example for testing newly methods proposed here. A statistical theorem referred to as Benford’s Law states that in many sets of numerical data, the significant digits are not uniformly distributed, as one might expect, but adhere to a certain logarithmic probability function. In order to detect fraud, we derive several requirements that should, according to this law, be fulfilled in the case of survey data.We show that in several SOEP subsamples, Benford’s Law holds for the available continuous data. For this analysis, we developed a measure that reflects the plausibility of the digit distribution in interviewer clusters. We are thus able to demonstrate that several interviews that were known to have been fabricated and therefore deleted in the original user data set can now be detected using this method. Furthermore, in one subsample, we use this method to identify a case of an interviewer falsifying ten interviews not previously detected by the fieldwork organization.

Data Diagnostics Using Second-Order Tests of Benford's Law

2009 ◽  
Vol 28 (2) ◽  
pp. 305-324 ◽  
Author(s):  
Mark J. Nigrini ◽  
Steven J. Miller
Keyword(s):  
Numerical Data ◽  
Second Order ◽  
Benford’S Law ◽  
Data Sets ◽  
Data Set ◽  
Accounting Data ◽  
Benford's Law ◽  
Rounded Data ◽  
Analytical Procedures ◽  
Digit Frequencies

SUMMARY: Auditors are required to use analytical procedures to identify the existence of unusual transactions, events, and trends. Benford's Law gives the expected patterns of the digits in numerical data, and has been advocated as a test for the authenticity and reliability of transaction level accounting data. This paper describes a new second-order test that calculates the digit frequencies of the differences between the ordered (ranked) values in a data set. These digit frequencies approximate the frequencies of Benford's Law for most data sets. The second-order test is applied to four sets of transactional data. The second-order test detected errors in data downloads, rounded data, data generated by statistical procedures, and the inaccurate ordering of data. The test can be applied to any data set and nonconformity usually signals an unusual issue related to data integrity that might not have been easily detectable using traditional analytical procedures.

scholarly journals THE LINK BETWEEN EARNINGS MANAGEMENT AND DIGITAL PATTERN / O elo entre gerenciamento de resultado e padrão digital

2014 ◽  
Vol 14 (1) ◽  
pp. 351
Author(s):  
Jennifer Martínez Ferrero ◽  
Beatriz Cuadrado Ballesteros ◽  
Marco Antonio Figueiredo Milani Filho
Keyword(s):  
Earnings Management ◽  
Financial Reporting ◽  
Reporting Quality ◽  
Poor Quality ◽  
Financial Data ◽  
Benford’S Law ◽  
Similar Distribution ◽  
Data Set ◽  
Benford's Law ◽  
High Degree

<p>According to Dechow and Dichev (2002) and Lin and Wu (2014), a high degree of earnings management (EM) is associated with a poor quality of information. In this sense, it is possible to assume that the financial data of companies that manage earnings can present different patterns from those with low degree of EM. The aim of this exploratory study is to test whether a financial data set (operating expenses) of companies with high degree of EM presents bias. For this analysis, we used the model of Kothari and the modified model of Jones (“Dechow model” hereafter) to estimate the degree of EM, and we used the logarithmic distribution of data predicted by the Benford’s Law to detect abnormal patterns of digits in number sets. The sample was composed of 845 international listed non-financial companies for the year 2010. To analyze the discrepancies between the actual and expected frequencies of the significant-digit, two statistics were calculated: Z-test and Pearson’s chi-square test. The results show that, with a confidence level of 90%, the companies with a high degree of EM according to the Kothari model presented similar distribution to that one predicted by the Benford’s Law, suggesting that, in a preliminary analysis, their financial data are free from bias. On the other hand, the data set of the organizations that manage earnings according to the Dechow model presented abnormal patterns. The Benford´s Law has been implemented to successfully detect manipulated data. These results offer insights into the interactions between EM and patterns of financial data, and stimulate new comparative studies about the accuracy of models to estimate EM.</p><p>Keywords:<strong> </strong>Earnings management (EM). Financial Reporting Quality (FRQ). Benford’s Law.</p>

Using Benford’s law on the seismic reflectivity analysis

2018 ◽  
Vol 6 (3) ◽  
pp. T689-T697
Author(s):  
Isadora A. S. de Macedo ◽  
Jose Jadsom S. de Figueiredo
Keyword(s):  
Geomagnetic Field ◽  
Point Of View ◽  
P Wave ◽  
Benford’S Law ◽  
Common Procedure ◽  
Data Set ◽  
Benford's Law ◽  
Distribution Point ◽  
Seismic Reflectivity ◽  
Reflectivity Data

Benford’s law (BL) is a mathematical theory of leading digits. This law predicts that the distribution of first digits of real-world observations is not uniform and follows a trend in which measurements with a lower first digit (1, 2, …) occur more frequently than those with higher first digits (…, 8, 9). A data set from earth’s geomagnetic field, the estimated time in years between reversals of earth’s geomagnetic field, the seismic P-wave speed of earth’s mantle below the southwest Pacific, and other geophysical data obey the BL. Although there are other statistical methods for analyzing a data set, we test, for the first time, the analysis of the seismic reflectivity through the Benford distribution point of view. We applied the BL on real reflectivity data from two wells from the Penobscot field and another two from the Viking Graben field. In both data sets, the reflectivity was in conformity with the BL. Moreover, after analyzing the effect of sonic and density logs despiking on Benford’s distribution through the BL, we found an optimum coefficient for the despiking process, which was a common procedure used to edit the well-log data before its use on reservoir studies.

scholarly journals Benford’s Law and Fraud Detection: Facts and Legends

2010 ◽  
Vol 11 (3) ◽  
pp. 397-401 ◽  
Author(s):  
Andreas Diekmann ◽  
Ben Jann
Keyword(s):  
Fraud Detection ◽  
Scientific Data ◽  
False Positives ◽  
Benford’S Law ◽  
Test Results ◽  
False Negatives ◽  
Benford's Law ◽  
Valid Test

Abstract Is Benford’s law a good instrument to detect fraud in reports of statistical and scientific data? For a valid test, the probability of ‘false positives’ and ‘false negatives’ has to be low. However, it is very doubtful whether the Benford distribution is an appropriate tool to discriminate between manipulated and non-manipulated estimates. Further research should focus more on the validity of the test and test results should be interpreted more carefully.

scholarly journals The Application of Benford’s Law in Fraud Detection: A Systematic Methodology

2019 ◽  
Vol 12 (10) ◽  
pp. 1
Author(s):  
Nirosh Kuruppu
Keyword(s):  
Fraud Detection ◽  
Benford’S Law ◽  
Financial Fraud ◽  
Spreadsheet Program ◽  
Naturally Occurring ◽  
Systematic Methodology ◽  
Benford's Law ◽  
The Cost ◽  
Financial Transactions

Benford&rsquo;s Law relies on a recently proven mathematical distribution about the frequencies of naturally occurring numbers that can be efficiently applied to the detection of financial fraud. Despite the value of Benford&rsquo;s Law for detecting fraud, most financial professionals are often unaware of its existence and how to best utilise the method for fraud detection. The purpose of this paper is therefore to present a systematic methodology for incorporating Benford&rsquo;s Law for detecting and flagging potentially fraudulent financial transactions, that can be further investigated. This paper describes the development of Benford&rsquo;s Law and demonstrates how it can be implemented systematically through a spreadsheet program to detect potential fraud. Given that the cost of financial fraud is significant with firms losing up to a tenth of their revenues, the methodology presented in this paper for implementing Benford&rsquo;s Law can be a valuable tool for auditors and other financial professionals for detecting fraud.

scholarly journals On the authenticity of COVID-19 case figures

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243123
Author(s):  
Adrian Patrick Kennedy ◽  
Sheung Chi Phillip Yam
Keyword(s):  
Empirical Evidence ◽  
Fraud Detection ◽  
Zipf’S Law ◽  
World Health ◽  
Benford’S Law ◽  
Surveillance Systems ◽  
Zipf's Law ◽  
Benford's Law ◽  
Formal Statistical Analysis ◽  
Health Organization

In this article, we study the applicability of Benford’s law and Zipf’s law to national COVID-19 case figures with the aim of establishing guidelines upon which methods of fraud detection in epidemiology, based on formal statistical analysis, can be developed. Moreover, these approaches may also be used in evaluating the performance of public health surveillance systems. We provide theoretical arguments for why the empirical laws should hold in the early stages of an epidemic, along with preliminary empirical evidence in support of these claims. Based on data published by the World Health Organization and various national governments, we find empirical evidence that suggests that both Benford’s law and Zipf’s law largely hold across countries, and deviations can be readily explained. To the best of our knowledge, this paper is among the first to present a practical application of Zipf’s law to fraud detection.

scholarly journals Testing the emission reduction claims of CDM projects using the Benford’s Law

2019 ◽  
Vol 160 (3) ◽  
pp. 407-426
Author(s):  
Matthew A. Cole ◽  
David J. Maddison ◽  
Liyun Zhang
Keyword(s):  
Frequency Analysis ◽  
Emission Reduction ◽  
Low Cost ◽  
Numerical Data ◽  
Benford’S Law ◽  
Data Manipulation ◽  
Digital Frequency ◽  
Benford's Law ◽  
Design Documents ◽  
Made In

AbstractBenford’s Law suggests that the first digits of numerical data are heavily skewed towards low numbers. Data that fail to conform to Benford’s Law when conformity is to be expected may have been manipulated. Using Benford’s Law, we conduct digital frequency analysis on the emission reduction claims of Clean Development Mechanism projects. Digital frequency analysis indicates that although emission reduction claims made in project design documents do not conform to Benford’s Law, we cannot reject the null hypothesis that data on certified emission reductions do. Benford’s Law offers a rapid, low-cost means of identifying possible instances of data manipulation.

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