Chapter 10 DETECTING FRAUD USING MODIFIED BENFORD ANALYSIS

Abstract Large enterprises frequently enforce accounting limits to reduce the im-pact of fraud. As a complement to accounting limits, auditors use Ben-ford analysis to detect traces of undesirable or illegal activities in ac-counting data. Unfortunately, the two fraud fighting measures often do not work well together. Accounting limits may significantly disturb the digit distribution examined by Benford analysis, leading to high false alarm rates, additional investigations and, ultimately, higher costs. To better handle accounting limits, this paper describes a modified Ben-ford analysis technique where a cut-off log-normal distribution derived from the accounting limits and other properties of the data replaces the distribution used in Benford analysis. Experiments with simulated and real-world data demonstrate that the modified Benford analysis tech-nique significantly reduces false positive errors