Preprint arXiv:2105.09812 [stat.AP]; last accessed May 31, 2021.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
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.
Bibtex:
@misc{,
title={Double-Crossing Benford's Law},
author={Javad Kazemitabar},
year={2021},
eprint={2105.09812},
archivePrefix={arXiv},
primaryClass={stat.AP},
url = {https://arxiv.org/abs/2105.09812},
}
Reference Type: Preprint
Subject Area(s): Accounting