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Benford's law has also been applied for forensic auditing and fraud detection on data from the 2003 California gubernatorial election,^[42] the 2000 and 2004 United States presidential elections,^[43] and the 2009 German federal election;^[44] the Benford's Law Test was found to be "worth taking seriously as a statistical test for fraud," although "is not sensitive to distortions we know significantly affected many votes."^[43]^[]

Amid allegations of electoral fraud in the 2016 Russian elections, an article co-written by Kirill Kalinin and Mebane in The Washington Post observed that the mean of the second digit of the number of voters in each of the country's 96,869 electoral precincts, to four significant figures, was equal to the expected mean (4.187) per Benford's law. In addition, the mean of the last digit of the votes in each precinct for the triumphant party, United Russia, was equal to the expected mean (4.5) per Benford's law. On the basis of other indicators of electoral fraud, Kalinin and Mebane suggest that these "perfect" statistics show that those responsible had deliberately rigged the votes to conform to the expectations of Benford's law.^[45]

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Benford’s Law has many real-world applications and, in fact, is admissible in court as a means of detecting fraud. Criminals who try to fabricate fraudulent lists of numbers typically aren’t aware of Benford’s Law and almost always don’t account for this statistical phenomena. Many people have been caught for financial dishonesty by applying this formula and it remains a significant component in the fraud-detection industry.