Business Intelligence SIG: Detection of Irregularities in Accounting Data
The Monthly Meeting of the Business Intelligence SIG
Large Scale Detection of Irregularities in Accounting Data
In recent years, there have been several large accounting frauds where a company's financial results have been intentionally misrepresented by billions of dollars. In response, regulatory bodies have mandated that auditors perform analytics on detailed financial data with the intent of discovering such misstatements. For a large auditing firm, this may mean analyzing millions of records from thousands of clients. In this talk, I will discuss techniques for automatic analysis of company general ledgers on such a large scale to identify irregularities -- which may indicate fraud or just honest errors -- for additional review by auditors. These techniques have been implemented in a prototype system, called Sherlock, which combines aspects of classification, anomaly detection, and signature identification.
About the Presenter
Stephen Bay, Center for Advanced Research, PricewaterhouseCoopers LLP, firstname.lastname@example.org
Stephen Bay is the analytics lead on Project Sherlock at PricewaterhouseCoopers LLP. The goal of Project Sherlock is to detect risks of potential financial statement error or fraud by uncovering anomalous activity in a company's general ledger. Stephen received his Ph.D. from U.C. Irvine in 2001, and Master's and Bachelor's degrees from the University of Waterloo. Prior to joining PricewaterhouseCoopers, Stephen worked at the Center for the Study of Language and Information at Stanford University, and the Institute for the Study of Learning and Expertise.
Cubberley Community Center
4000 Middlefield Road, Room H-1
Palo Alto, CA
6:30 - 7:00 p.m. Registration / Networking / Refreshments (please arrive before 7:00 p.m.)
7:00 - 8:30 p.m. Presentation and Discussion
$15 at the door for non-SDForum members
No charge for SDForum members
No registration required
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