Abiding by the Law? Using Benford’s Law to Examine the Accuracy of Nonprofit Financial Reports

2019 ◽  
Vol 49 (3) ◽  
pp. 548-570 ◽  
Author(s):  
Heng Qu ◽  
Richard Steinberg ◽  
Ronelle Burger
Keyword(s):  
Statistical Methods ◽  
Organizational Level ◽  
Benford’S Law ◽  
Financial Reports ◽  
Forensic Accounting ◽  
Data Form ◽  
Benford's Law ◽  
Natural Data ◽  
The Individual ◽  
Academic Study

Benford’s Law asserts that the leading digit 1 appears more frequently than 9 in natural data. It has been widely used in forensic accounting and auditing to detect potential fraud, but its application to nonprofit data is limited. As the first academic study that applies Benford’s Law to U.S. nonprofit data (Form 990), we assess its usefulness in prioritizing suspicious filings for further investigation. We find close conformity with Benford’s Law for the whole sample, but at the individual organizational level, 34% of the organizations do not conform. Deviations from Benford’s law are smaller for organizations that are more professional, that report positive fundraising and administration expenses, and that face stronger funder oversight. We suggest improved statistical methods and experiment with a new measure of the extent of deviation from Benford’s Law that has promise as a more discriminating screening metric.

scholarly journals Detecting the dubious digits: Benford's law in forensic accounting

Significance ◽  
2007 ◽  
Vol 4 (2) ◽  
pp. 81-83 ◽  
Author(s):  
Kuldeep Kumar ◽  
Sukanto Bhattacharya
Keyword(s):  
Benford’S Law ◽  
Forensic Accounting ◽  
Benford's Law

Forensic Accounting and Benford's Law [In the Spotlight]

2008 ◽  
Vol 25 (2) ◽  
pp. 152-150 ◽  
Author(s):  
Sukanto Bhattacharya ◽  
Kuldeep Kumar
Keyword(s):  
Benford’S Law ◽  
Forensic Accounting ◽  
Benford's Law

Evaluation of Large-scale Data to Detect Irregularity in Payment for Medical Services

2016 ◽  
Vol 55 (03) ◽  
pp. 284-291
Author(s):  
Junghyun Park ◽  
Seokjoon Yoon ◽  
Minki Kim
Keyword(s):  
Large Scale ◽  
Healthcare Sector ◽  
Benford’S Law ◽  
Individual Level ◽  
Large Scale Data ◽  
Level Data ◽  
Depth Analysis ◽  
Benford's Law ◽  
The Individual ◽  
Scale Data

SummaryBackground: Sophisticated anti-fraud systems for the healthcare sector have been built based on several statistical methods. Although existing methods have been developed to detect fraud in the healthcare sector, these algorithms consume considerable time and cost, and lack a theoretical basis to handle large-scale data.Objectives: Based on mathematical theory, this study proposes a new approach to using Benford’s Law in that we closely examined the individual-level data to identify specific fees for in-depth analysis.Methods: We extended the mathematical theory to demonstrate the manner in which large-scale data conform to Benford’s Law. Then, we empirically tested its applicability using actual large-scale healthcare data from Korea’s Health Insurance Review and Assessment (HIRA) National Patient Sample (NPS). For Benford’s Law, we considered the mean absolute deviation (MAD) formula to test the large-scale data.Results: We conducted our study on 32 diseases, comprising 25 representative diseases and 7 DRG-regulated diseases. We performed an empirical test on 25 diseases, showing the applicability of Benford’s Law to large-scale data in the healthcare industry. For the seven DRG-regulated diseases, we examined the individual-level data to identify specific fees to carry out an in-depth analysis. Among the eight categories of medical costs, we considered the strength of certain irregularities based on the details of each DRG-regulated disease.Conclusions: Using the degree of abnormality, we propose priority action to be taken by government health departments and private insurance institutions to bring unnecessary medical expenses under control. However, when we detect deviations from Benford’s Law, relatively high contamination ratios are required at conventional significance levels.

scholarly journals Empirical Analysis of Claims Development Trapezoids following Benford’s Law

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Jochen Heberle ◽  
Tobias Gummersbach
Keyword(s):  
Hypothesis Testing ◽  
Empirical Analysis ◽  
Individual Development ◽  
Benford’S Law ◽  
Characteristic Factor ◽  
Wide Range ◽  
Benford's Law ◽  
The Individual

In this paper we make an empirical analysis of a wide range of claims developmenttrapezoids following Benford’s law. In particular we determine Benfors’s law fordifferent characteristic factors depending on claims development triangles/trapezoids.These characteristic factors are the cumulative claims payments, the incrementalclaims payments and the individual development factors. For each characteristic factor hypothesis testing is done for verifying/rejecting Benford’s law.

scholarly journals The empirical analysis of financial reports of companies in Croatia: Benford distribution curve as a benchmark for first digits

2019 ◽  
Vol 5 (2) ◽  
pp. 90-100
Author(s):  
Ivana Cunjak Mataković
Keyword(s):  
Distribution Curve ◽  
Stock Exchange ◽  
Financial Statements ◽  
Benford’S Law ◽  
Chi Square ◽  
Financial Reports ◽  
Benford's Law ◽  
Chi Square Test ◽  
Kolmogorov Smirnov ◽  
The Empirical Analysis

AbstractThe financial numbers game is unfortunately alive and doing well. One of the forensic accounting techniques is based on Benford’s Law and is used for the detection of unusual transactions, anomalies or trends. The aim of this paper is to test whether the financial statements of Croatian companies deviate from Benford’s Law distribution. The financial statements of 24 companies that are in the pre-bankruptcy settlement process and 24 companies that are not in the pre-bankruptcy settlement process were analysed using the Benford’s Law test of the first digit distribution for the period from 2015 to 2018. The data used to calculate the first digits of distribution were taken from the Zagreb Stock Exchange. The chi-square test has shown that the observed companies that are not in the process of pre-bankruptcy settlement do not have the first digit distribution which follows the Benford’s Law distribution. The Kolmogorov-Smirnov Z test has shown that the distribution of the first digits from the financial statements of companies listed on the Zagreb Stock Exchange fits to Benford’s Law distribution.

Psychology and Benfordʼs Law

Benford's Law ◽  
2015 ◽  
Author(s):  
Bruce D. Burns ◽  
Jonathan Krygier
Keyword(s):  
Decision Making ◽  
Benford’S Law ◽  
Test Case ◽  
Statistical Relationship ◽  
Common Theme ◽  
The Public ◽  
Benford's Law ◽  
Natural Data ◽  
Statistical Relationships ◽  
Practical Implications

This chapter outlines recent research showing that people can approximate Benford's law when generating meaningful numbers in psychology. A common theme in recent research into reasoning and decision-making has been that people are influenced by statistical relationships in the environment. However, because it is hard to know the precise statistical relationships an individual has experienced over their lifetime, rarely is it possible to test whether people are truly acting precisely in accord with an unknown naturally occurring statistical relationship. Benford's law provides an interesting test case in this regard because it is a precise statistical relationship that is both widespread and little known to the public. Hence, the chapter reveals that Benford's law has theoretical implications for decision-making research, practical implications for fraud detection, and may help cast light on Benford's law as a property of natural data.

EARNINGS MANAGEMENT AMONG FIRMS DURING THE PRE-SEC ERA: A BENFORD'S LAW ANALYSIS

2011 ◽  
Vol 38 (2) ◽  
pp. 145-170 ◽  
Author(s):  
Jeffrey J. Archambault ◽  
Marie E. Archambault
Keyword(s):  
Financial Statement ◽  
Benford’S Law ◽  
Operating Environment ◽  
Industrial Firms ◽  
Financial Reports ◽  
Rate Regulation ◽  
Time Period ◽  
Benford's Law ◽  
Utility Companies ◽  
Net Assets

ABSTRACT This paper examines the existence of financial statement manipulation in the U.S. during a time period when many of the current motivations did not exist. The study looks for types of manipulations that would be motivated by the pre-SEC operating environment. To examine this issue, a sample of U.S. firms from the 1915 Moody's Analyses of Investments is divided into industrial firms, railroads, and utilities. The railroad and utility companies faced rate regulation during this time period, providing incentives to manipulate the financial reports so as to maximize the rate received. Industrial firms were not regulated. These companies wanted to attract investors, motivating manipulations to increase income and net assets. To determine if manipulations are occurring, a Benford's Law analysis is used. This analysis examines the frequency of numbers in certain positions within an amount to determine if the distribution of the numbers is similar to the pattern documented by Benford's Law. Some manipulations consistent with expectations are found.

scholarly journals Fraud Detection in Financial Statements Applying Benford's Law with Monte Carlo Simulation

2019 ◽  
Vol 69 (2) ◽  
pp. 217-239
Author(s):  
Vladan Pavlović ◽  
Goranka Knežević ◽  
Marijana Joksimović ◽  
Dušan Joksimović
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Frequency Distribution ◽  
High Probability ◽  
Financial Statements ◽  
Financial Statement ◽  
Benford’S Law ◽  
Financial Reports ◽  
Benford's Law ◽  
Very High

Benford's Law is a useful tool for detecting fraud in financial statements. In this paper we test the financial item named ‘Work performed by the undertaking for its own purpose and capitalised’ applying this tool. The data are taken from the financial reports of all companies submitted to the Serbian Business Register Agency for the period of 2008–2013. Our conclusion shows that there is a very high probability that the frequency distribution of the second digit does not satisfy Benford's Law. In other words, it implies that certain manipulations have been usually done with the second digit of the aforementioned item in the financial statement. This research confirms our hypothesis that financial statement frauds are usually conducted using the second digit.

Benford's Law

2015 ◽  
Keyword(s):  
Stock Prices ◽  
Mortality Rates ◽  
Benford’S Law ◽  
Data Sets ◽  
Financial Reports ◽  
Medical Tests ◽  
Benford's Law ◽  
Technical Details ◽  
The Many ◽  
Population Numbers

Benford's law states that the leading digits of many data sets are not uniformly distributed from one through nine, but rather exhibit a profound bias. This bias is evident in everything from electricity bills and street addresses to stock prices, population numbers, mortality rates, and the lengths of rivers. This book demonstrates the many useful techniques that arise from the law, showing how truly multidisciplinary it is, and encouraging collaboration. Beginning with the general theory, the chapters explain the prevalence of the bias, highlighting explanations for when systems should and should not follow Benford's law and how quickly such behavior sets in. The book goes on to discuss important applications in disciplines ranging from accounting and economics to psychology and the natural sciences. The book describes how Benford's law has been successfully used to expose fraud in elections, medical tests, tax filings, and financial reports. Additionally, numerous problems, background materials, and technical details are available online to help instructors create courses around the book. Emphasizing common challenges and techniques across the disciplines, this book shows how Benford's law can serve as a productive meeting ground for researchers and practitioners in diverse fields.

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