Stanford Talk: Large Scale Detection of Irregularities in Accounting Data

Large Scale Detection of Irregularities in Accounting Data

Stephen Bay, Center for Advanced Research, PricewaterhouseCoopers LLP

Abstract:
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 both outlier detection and classification. In developing Sherlock, we faced three major challenges: developing an efficient process for obtaining data from many heterogeneous sources, training classifiers with only positive and unlabeled examples, and presenting information to auditors in an easily interpretable manner.