Verifying Numerical Data for Fraudulent Alterations: Benford's Law Test - Essay Sample

Introduction

A company’s data is prone to fraudulent alterations. This mainly includes numerical data that people can take advantage of and manipulate it to their advantage. This may, in return, affect the company's finances. The false information may also go a long way in misstating the company's financial position. However, there are many ways in which the numerical data can be verified and crosschecked for any fraudulent alterations. A standard method is Benford’s Law Test, which is used here to identify fraud in two sets of information.

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Benford’s Law says that there's a certain way in which numbers are going to appear in the natural course of the universe. Excel’s CHIPSET formula is used to demonstrate whether the set of data follows the required Benford distribution. High percentage values indicate a good match, whereas a low percentage value signals fraudulent alterations and anomalies in the data. This technique is of great importance as it helps to deal with the fraud that may have an overall impact on the company. Ozigbo (2015) maintained that fraudulent information might have short-term or long-term effects on the company. Financial loss is a significant problem that may arise. The difficulty in tracking the finances of a company is caused by increased business complexity and bad faith in the employees. The Benfords Law seeks to address this by verifying information and rating it against a scale of accuracy. (Jamaal, 2008).

According to Jamaal (2008), this technique has the value of maintaining business continuity by detecting fraud in financial information. It helps keep all financial records in check. It unearths patterns of fraud and common anomalies in the report.

In our test, both company A and B ha fraudulent invoice information. In company A, digit 5 had the highest frequency at 22. Digit 8 had the lowest rate at 2. Both numbers 6 and 7 shared a frequency of 11. Digit 3 had a frequency of 9, and digit 4 had a frequency of 10. Digit 8 had a frequency of 2, and number 9 had a frequency of 4. Digit 2 had a frequency of 3. Both the Benford’s rate and actual data rate were rounded off to 4 significant figures during the evaluation test. Using the chi-square probability function in Excel, the result of company A data was 0.09957. This being a low percentage demonstrates that the information was fraudulent.

For company B. The digit frequencies were as follows. Digit 6 had the highest rate at 23. Digit 0 and 9 had the lowest frequencies at 0. Both digit 1 and 3 had a frequency of 2. Digit 2 had a frequency of 13, and figure 4 had a rate of 7. Digit 5 had a frequency of 7. High fraudulence was evident in company B data. The percentage was at 0.9994, which is significantly low.

Using Benford’s law test, the data sets for both companies appeared to have flaws. The fraud levels, too, were determined. In conclusion, Benford’s Law is a useful technique in deciding whether a set of data is fraudulent. This can, in turn, help a company develop decisions based on the results produced by Benford’s Law. (Nigrini, 2012).

References

Jamal, K. (2008). Mandatory audit of financial reporting: A failed strategy for dealing with fraud. Accounting Perspectives, 7(2), 2013-680.

Ozigbo, S. A. (2015). Internal control and fraud prevention in Nigerian business organizations: A survey of some selected companies in Warri metropolis. Journal of Policy and Development Studies, 289(1851), 1-4.

Nigrini, M. J. (2012). Benford's Law: Applications for forensic accounting, auditing, and fraud detection (Vol. 586). John Wiley & Sons.