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.

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.

Introduction

2015 ◽  
Author(s):  
Arno Berger ◽  
Theodore P. Hill
Keyword(s):  
Numerical Data ◽  
Benford’S Law ◽  
Decimal Digit ◽  
Mathematical Framework ◽  
Benford's Law ◽  
History Of ◽  
Logarithmic Distribution ◽  
Benford Law ◽  
Special Case

This introductory chapter provides an overview of Benford' law. Benford's law, also known as the First-digit or Significant-digit law, is the empirical gem of statistical folklore that in many naturally occurring tables of numerical data, the significant digits are not uniformly distributed as might be expected, but instead follow a particular logarithmic distribution. In its most common formulation, the special case of the first significant (i.e., first non-zero) decimal digit, Benford's law asserts that the leading digit is not equally likely to be any one of the nine possible digits 1, 2, … , 9, but is 1 more than 30 percent of the time, and is 9 less than 5 percent of the time, with the probabilities decreasing monotonically in between. The remainder of the chapter covers the history of Benford' law, empirical evidence, early explanations and mathematical framework of Benford' law.

scholarly journals How Valid are the Reported Cases of People Infected with Covid-19 in the World?

2020 ◽  
Vol 1 (2) ◽  
pp. 53-56
Author(s):  
Raul Isea
Keyword(s):  
United Kingdom ◽  
Forensic Analysis ◽  
Benford’S Law ◽  
P Values ◽  
Data Manipulation ◽  
Digital Forensic ◽  
Analysis Technique ◽  
The World ◽  
Benford's Law ◽  
Watson Test

The goal of this paper is to analyze the registered cases of people who have been infected with Covid-19 registered from throughout the world, using a digital forensic analysis technique that is based on Benford's Law. Twenty-three countries were randomly chosen for this analysis: China, India, Germany, Brazil, Venezuela, Netherlands, Italy, Colombia, Russia, Norway, South Africa, Portugal, Singapore, United Kingdom, Chile, Ecuador, Egypt, Denmark, Ireland, France, Belgium, Australia and Croatia.. We calculate on the p-values based on Pearson χ2 and Mantissa Arc Test according to the results obtained with the first digit. If any country fails these two tests, a third proof will be carried out based on the Freedman-Watson test. The results indicated that results from Italy, Portugal, Netherlands, United Kingdom, Denmark, Belgium and Chile are suspicions of data manipulation because the numbers fail the Benford’s Law according to the results obtained until April 30, 2020. However, it is necessary to carry out further studies in these countries in order to ensure that they countries manipulate or altered the information.

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.

Audit Sampling Using Benford's Law: A Review of the Literature with Some New Perspectives

2017 ◽  
Vol 14 (2) ◽  
pp. 29-46 ◽  
Author(s):  
Mark J. Nigrini
Keyword(s):  
Sampling Method ◽  
Numerical Data ◽  
Financial Statement ◽  
Benford’S Law ◽  
Review Of The Literature ◽  
Financial Statement Fraud ◽  
Accuracy Rate ◽  
Benford's Law ◽  
Red Flag ◽  
Audit Sampling

ABSTRACT Accounting studies have used the premise that nonconformity to Benford's Law (hereafter, Benford), which gives the expected patterns of the leading digits in numerical data, is a red flag for fraud. This study reviews Benford's Law and divides the accounting applications into five categories. A proposed Benford-based audit sampling method, which selects as the audit sample the set of transactions or balances that needs to be removed from the audit population to leave a remainder that conforms to Benford, is reviewed and reexamined. The finding is that the method, as advocated, can generate large audit samples and that the accuracy rate is questionable, even when known errors are seeded into the data. The study then reviews some new perspectives on using Benford's Law in auditing by reviewing (1) the mathematical bases for expecting Benford conformity, (2) the type of auditee data that are appropriate for Benford-based sampling, (3) various options to limit the sample size, and (4) the limitations of a Benford-based sampling approach. These perspectives draw on some facts related to the way in which the HealthSouth Corporation financial statement fraud was executed.

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