From Covid-19 mortality rate to image tampering, Benford's law is used to detect fraudulent activities. The underlying assumption for using the law is that a ``regular" dataset follows the significant digit phenomenon. In this paper, we address the scenario where a shrewd fraudster manipulates a list of numbers in such a way that while providing the desired statistics, it still complies with Benford's law. We develop a framework that offers several degrees of freedom to such a fraudster, such as the minimum, maximum, mean, and size of the manipulated dataset. The conclusion further corroborates the idea that Benford's law -if at all- should be used with utmost discretion as a means for fraud detection.
Kazemitabar, J. (2025). Double-crossing Benford's law. Journal of Statistical Modelling: Theory and Applications, 6(1), 49-57. doi: 10.22034/jsmta.2025.22275.1162
MLA
Kazemitabar, J. . "Double-crossing Benford's law", Journal of Statistical Modelling: Theory and Applications, 6, 1, 2025, 49-57. doi: 10.22034/jsmta.2025.22275.1162
HARVARD
Kazemitabar, J. (2025). 'Double-crossing Benford's law', Journal of Statistical Modelling: Theory and Applications, 6(1), pp. 49-57. doi: 10.22034/jsmta.2025.22275.1162
CHICAGO
J. Kazemitabar, "Double-crossing Benford's law," Journal of Statistical Modelling: Theory and Applications, 6 1 (2025): 49-57, doi: 10.22034/jsmta.2025.22275.1162
VANCOUVER
Kazemitabar, J. Double-crossing Benford's law. Journal of Statistical Modelling: Theory and Applications, 2025; 6(1): 49-57. doi: 10.22034/jsmta.2025.22275.1162
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