A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud

TLDR

The study proposes a genetic algorithm to detect financial statement fraud. The method uses a genetic algorithm trained on 51 SEC‑accused firms and 339 matched peers, employing 76 comparative financial metrics and nine firm characteristics. The algorithm correctly classifies 63% of fraud‑suspected firms and 95% of non‑fraud firms. © 2007 John Wiley & Sons, Ltd.

Abstract

Abstract This study presents a genetic algorithm approach to detecting financial statement fraud. The study uses a sample comprising a target class of 51 companies accused by the Securities and Exchange Commission of improperly recognizing revenue and a peer class of 339 companies matched on industry and size (revenue). Variables include 76 comparative metrics, based on specific financial metrics and ratios that capture company performance in the context of historical and industry performance, and nine company characteristics. Time‐based patterns detected by the genetic algorithm accurately classify 63% of the target class companies and 95% of the peer class companies. Copyright © 2007 John Wiley & Sons, Ltd.