NUMBERS DON’T LIE: DECODING FINANCIAL ERROR AND FRAUD THROUGH BENFORD’S LAW

Abstract

This research investigates Benford’s Law as a statistical instrument to detect financial fraud and errors. Benford's Law, also known as the First-Digit Law, states that lesser digits, specifically '1', frequently appear as the leading digit in numerous numerical datasets, and deviations from this distribution may indicate potential financial irregularities. This research examines literature demonstrating the application and efficacy of Benford’s Law in identifying numerical inconsistencies indicative of financial misconduct. This research investigates the use of Benford’s Law as a tool of detecting error and fraudulent activities within the sales data of two branches during 2022. A comprehensive dataset of 3098 records from Bandung and 539 records from Surabaya was collected after excluding certain data points that exhibited abnormalities. The application of Benford's statistical test discovered a discrepancy between the Benford probability and the observed probability, suggesting the presence of possible errors and frauds. The audit findings unveiled anomalies in pricing and instances of fraudulent activities in both locations, primarily due to pricing discrepancies and incorrect price inputs from sales orders. Furthermore, instances of fraud involved the manipulation of set prices for personal gain by salesmen. The results affirmed the hypothesis that a larger deviation between Benford’s probability and the observed probability corresponded with a higher incidence of error and fraud. However, it observes that Benford’s Law is not a stand-alone solution for detecting fraud, as not all financial datasets conform to it and deviations from the law only indicate the possibility of fraud, not confirm it. Therefore, the research suggests using Benford’s Law in conjunction with other data analysis and auditing techniques to conduct a comprehensive investigation. The conclusion of research emphasizes the significance of Benford’s Law in the field of forensic accounting and the need for multidimensional strategies for effective error and fraud detection.