Undergraduate Winter Research Project, Government College of Engineering and TextileTechnology, Serampore, India.
ISSN/ISBN: Not available at this time. DOI: 10.2139/ssrn.4175594
Abstract: With the increase in demands and price of goods and services, fraudulency has caught a great height. Now, it can’t be prohibited completely in the first stage. The detection of fraud have attracted continuous attention from academia, industry and regulatory agencies, and it is a challenging task for the researchers to develop a fraud detection framework. Starting from the late 1900s, ‘Benford’s law’ has served this purpose well. Abruptly, within a decade of its application lots and lots of fraudulency started getting seized. Later on, this law was used for detecting fairness of the elections, forensics, finances, etc. This article proposes a formula specifically derived from Zipf’s Law that can detect fairness and fallacies in datasets involving forensics, finances, elections, and similar socio-economic issues. Unlike Benford’s Law, our proposed formula is not dependent on any sort of observations, rather it is backboned by rigorous proof. Finally, we have done a comparison analysis between Benford’s Law and our proposed formula graphically. All the data sets used by us have been rigorously studied, and many fitting tests have been applied to them.
Bibtex:
@thesis{,
AUTHOR = {Manan Roy Choudhury and Manik Mondal},
TITLE ={An Affiliated Approach to Data Validation: US 2020 Governor’s County Election},
SCHOOL = {Government College of Engineering and TextileTechnology},
YEAR = {2022},
ADDRESS = {Serampore, India},
TYPE = {Undergraduate Winter Research Project},
DOI = {10.2139/ssrn.4175594},
URL = {https://ssrn.com/abstract=4175594},
}
Reference Type: Thesis
Subject Area(s): Accounting, Voting Fraud