FRAUD DETECTION AND ANALYSIS USING BENFORD’S LAW

Abstract

Fraud is any risky activity that aims to cause financial loss to another person. Fraud occurs as a result of deliberately used data in a complex cyber-complex operation that is a difficult task to find or research agencies. However, a ban on risky fraud is the best way to reduce fraud. The complexity of the supply network details allows fraudsters to commit fraud beyond internal control. Detecting fraud by analysing large amounts of data is a difficult task to find or evaluate agencies. Intension to use Excel Sheet to conduct Benford’s Law distribution statistics tests as an effective tool for detecting red flags on suspected data. Supportive tools, leads to inaccurate detection or hidden data patterns, and its in-depth analysis helps agencies to scientifically assess the feasibility of using a single trust-but verification’ approach to a delivery network using a possible distribution called Benford’s Law distribution within a short timeframe. and to prevent fraudulent transactions. Applications of Benford’s Law The basic application of Benford’s law is in fraud detection. Here are some of the most popular fraud detection applications of the law: ● Accounting: The idea behind detecting using Benford’s law is that, if data of a certain type is known to be close to Benford’s law, the chi-squared test comes to the rescue to identify the fraud. ● Election: The law has been used in detecting election frauds in many country elections. ● Banks: Deutsche Bank said Benford’s Law works on balance sheets. The bank said that the law applies equally well to balance sheets and income statements as it does on another dataset. ● Approximation of mathematical models: Tests for goodness-of-fit to Benford are also useful as a diagnostic tool for assessing the appropriateness of mathematical models.