With the increase in demands and price of goods and services, fraudulency has caught a great height. But, it can’t be prohibited completely in the first stage. The detection of fraud has 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 socioeconomic 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.