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An interesting tool called Benford\u2019s Law can be effective in detecting fraud. But it also can be used during external auditing procedures to test journal entries for unusual numeric patterns. Here\u2019s what you should know about this statistical technique, including its potential limitations.<\/p>\n
Random data sets<\/strong><\/p>\n According to Benford\u2019s Law, in sets of random data, numbers beginning with smaller digits occur more frequently. For example, numbers beginning with 1 occur about 30% of the time, numbers beginning with 2 occur about 18% of the time, and so on, down to numbers beginning with 9, which occur less than 5% of the time. The law also makes predictions about the distribution of second digits, third digits and digit combinations.<\/p>\n These patterns become skewed when dishonest workers attempt to manipulate numbers in certain financial documents. In fact, it\u2019s nearly impossible to manually enter data so that it conforms to Benford\u2019s Law. So, auditors may be able to use Benford\u2019s Law to test journal entries made for the following items:<\/p>\n If anomalies appear when performing this analysis, auditors will perform analytical review procedures to determine whether specific unusual circumstances, business changes, random fluctuations or misstatements may have impacted the data set. And they may need to consider whether alternative audit procedures \u2014 such as physically tracing transactions to supporting documentation or comparing the transactions to prior years\u2019 data \u2014 can be used to assess the validity of a questionable data set.<\/p>\n Spreadsheet analysis<\/strong><\/p>\n <\/strong>When applying Benford\u2019s Law, auditors typically run a spreadsheet program on the data set to examine the distribution of digits in random sets of numbers. By doing this, they calculate the frequency with which the digits 1 through 9 occur. The spreadsheet can be converted into a chart that highlights any significant deviations from the patterns the rule predicts.<\/p>\n For example, a chart that shows that 20% of the numbers in a data set begin with 9 and only 10% begin with 1 may indicate financial misstatement. But it doesn\u2019t prove<\/em> wrongdoing. Often, innocent explanations \u2014 such as duplicate entries and other human errors \u2014 lie behind suspicious patterns. That\u2019s why it\u2019s essential to dig deeper to understand what\u2019s gone awry.<\/p>\n Limitations <\/strong><\/p>\n Beware: Benford\u2019s Law sometimes generates false positive and negative results. Examples of confounding variables include:<\/p>\n Small data sets.<\/strong> There must be enough journal entries to be statistically relevant. For instance, if a receivables clerk falsifies just one or two journal entries, the impropriety is unlikely to be caught with Benford\u2019s Law.<\/p>\n Assigned numbers.<\/strong> The data being tested must occur naturally for Benford\u2019s Law to work. Nonrandom numbers that were created by humans \u2014 such as invoice numbers \u2014 won\u2019t conform to the prescribed pattern.<\/p>\n Artificial limits.<\/strong> If transactions are subjected to a floor or ceiling, some numbers won\u2019t occur in the data set. For instance, if petty cash draws can\u2019t exceed $50, the petty cash ledger won\u2019t conform to Benford\u2019s Law.<\/p>\n Also consider review thresholds. If a second signature is required on all disbursements greater than $5,000, the auditor might detect an unusually high occurrence of \u201c4\u201d in the first-digit position.<\/p>\n For more information<\/strong><\/p>\n Despite its limitations, Benford\u2019s Law can be a simple, cost-effective tool to test for data manipulation, processing inefficiencies and errors. Contact us to discuss whether this analysis can enhance your financial reporting. We can identify the types of transactions that are best suited for Benford\u2019s Law-based testing.<\/p>\n \u00a9 2022<\/em><\/p>\n <\/body> An interesting tool called Benford\u2019s Law can be effective in detecting fraud. But it also can be used during external auditing procedures to test journal entries for unusual numeric patterns. Here\u2019s what you should know about this statistical technique, including its potential limitations. Random data sets According to Benford\u2019s Law, in sets of random data, […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13,7,10],"tags":[8,11,12],"class_list":["post-16337","post","type-post","status-publish","format-standard","hentry","category-aa","category-articles","category-news","tag-articles","tag-news","tag-updates"],"_links":{"self":[{"href":"https:\/\/www.sfw.cpa\/news-and-guides\/wp-json\/wp\/v2\/posts\/16337","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sfw.cpa\/news-and-guides\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sfw.cpa\/news-and-guides\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sfw.cpa\/news-and-guides\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sfw.cpa\/news-and-guides\/wp-json\/wp\/v2\/comments?post=16337"}],"version-history":[{"count":1,"href":"https:\/\/www.sfw.cpa\/news-and-guides\/wp-json\/wp\/v2\/posts\/16337\/revisions"}],"predecessor-version":[{"id":16338,"href":"https:\/\/www.sfw.cpa\/news-and-guides\/wp-json\/wp\/v2\/posts\/16337\/revisions\/16338"}],"wp:attachment":[{"href":"https:\/\/www.sfw.cpa\/news-and-guides\/wp-json\/wp\/v2\/media?parent=16337"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sfw.cpa\/news-and-guides\/wp-json\/wp\/v2\/categories?post=16337"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sfw.cpa\/news-and-guides\/wp-json\/wp\/v2\/tags?post=16337"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
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