Double-Crossing Benford's Law
Applications
2021-05-21 v1
Abstract
Benford's law is widely used for fraud-detection nowadays. 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 still complies with Benford's law. We develop a general family of distributions that provides several degrees of freedom to such a fraudster such as minimum, maximum, mean and size of the manipulated dataset. The conclusion further corroborates the idea that Benford's law should be used with utmost discretion as a means for fraud detection.
Keywords
Cite
@article{arxiv.2105.09812,
title = {Double-Crossing Benford's Law},
author = {Javad Kazemitabar},
journal= {arXiv preprint arXiv:2105.09812},
year = {2021}
}