Financial Fraud Detection Now As Simple As 1, 2, 3 New Approach Exposes Irregularities in Annual Financial Statements and Can Help the SEC, Investors and Auditors Detect Fraud Faster
NEW YORK, May 21, 2014 /PRNewswire/ -- In an era of financial scandal headlines, has a reliable, cost-effective way to identify financial reporting fraud, as seen by the likes of Enron and WorldCom, been discovered? Researchers at Columbia Business School and The Ohio State University believe so and have created a new way for financial regulators and investors to "red flag" irregularities in financial statements faster and more efficiently than ever before.
"In the last decade the SEC has dramatically reduced its already sparse resources for detecting accounting fraud," said Prof. Dan Amiram, an assistant professor of accounting from Columbia Business School. "Accounting fraud is a threat to businesses and investors across the globe that, as some studies have suggested, costs investors billions of dollars annually. Our approach is easy to implement, effective, and much needed given the current rise in the volume and amount of electronically-filed corporate disclosure. "
Created by Professor Amiram, Ph.D. student Ethan Rouen, from Columbia Business School and Professor Zahn Bozanic from The Ohio State University's Fisher College of Business, the groundbreaking approach provides a novel way to identify irregularities in financial statements by examining how the numbers in these statements relate to naturally occurring statistical properties. Unlike existing strategies to detect fraud that can be gamed by determined managers, this new approach has no specific relationship to a company's business model, making it more difficult to fool and potentially leading to the unearthing of numerous undetected frauds in the U.S.
"Our approach complements the SEC's Accounting Quality Model, better known by its street name of 'Robocop'," said Prof. Bozanic, an assistant professor of accounting and management information systems from The Ohio State University. "It allows the SEC to quickly flag financial reports for review as soon as they are filed on Edgar."
New Prescriptions to Detect Fraud
The researchers identified three steps that significantly improved fraud detection. These included:
1. Applying a Well Known Tool for a New Purpose
For many years, forensic accountants looking for potential financial fraud in a company's internal books have relied on Benford's Law. The law looks for recurrent numerical patterns to detect fraud. For example, if the dollar amounts in a company's financial statements contain as many numbers beginning with five as they do beginning with three, it's a red flag for auditors to further investigate the numbers, which may have been invented. The new approach is the first of its kind to apply Benford's Law to annual financial statements.
2. Spotting Irregularities with a Special Score
The new approach can also help the SEC, auditors and investors generate a Financial Statement Divergence (FSD) score. A higher FSD score indicates a greater likelihood of financial irregularities. Investors, auditors, and regulators would be able to use the FSD Score to quickly and efficiently spot irregularities in financial reporting in a cost-effective manner. The FSD score has numerous advantages over existing measures as it does not require forward looking information and is available to virtually every firm with accounting information.
3. Saving Time and Improving Quality of Misstatements Discovery
There is a significant time lag between the occurrence of fraudulent financial reporting and SEC enforcement actions. As the FSD score is predictive of SEC Accounting and Auditing Enforcement Releases (AAERs), it can help to close this gap while accelerating and improving the quality of the auditing process itself.
The Research Paper and Results
The research paper is titled, "Financial Statement Irregularities: Evidence from the Distributional Properties of Financial Statement Numbers." Amongst other results, the authors show that once misstatement firms have issued corrected financial statements, the restated financial statements have significantly lower divergence from Benford's Law, as exhibited by lower FSD Scores. Importantly, the authors find that FSD scores successfully predict which firms materially misstate their financial statements. The complete paper and set of results can be downloaded from SSRN by clicking here.
About Columbia Business School
Columbia Business School is the only world–class, Ivy League business school that delivers a learning experience where academic excellence meets with real–time exposure to the pulse of global business. Led by Dean Glenn Hubbard, the School's transformative curriculum bridges academic theory with unparalleled exposure to real–world business practice, equipping students with an entrepreneurial mindset that allows them to recognize, capture, and create opportunity in any business environment. The thought leadership of the School's faculty and staff, combined with the accomplishments of its distinguished alumni and position in the center of global business, means that the School's efforts have an immediate, measurable impact on the forces shaping business every day. To learn more about Columbia Business School's position at the very center of business, please visit www.gsb.columbia.edu.
About The Ohio State University Fisher College of Business
From business as usual to business unusual, Fisher College of Business prepares students to go beyond and make an immediate impact in their careers through top-ranked programs, distinguished faculty, and a vast network of partnerships that reaches from the surrounding business community to multinationals, nonprofits and startups across the globe. Our students are uniquely prepared and highly sought, leveraging Fisher's rigorous, experiential learning environment with the resources of Ohio State, a premiere research university with 500,000 proud Buckeye alumni. To learn more about Fisher College of Business, please visit www.fisher.osu.edu.
SOURCE Columbia Business School