Affiliation:
1. University of San Diego
2. RWTH Aachen University
Abstract
ABSTRACT
Developing models to detect financial statement fraud involves challenges related to (1) the rarity of fraud observations, (2) the relative abundance of explanatory variables identified in the prior literature, and (3) the broad underlying definition of fraud. Following the emerging data analytics literature, we introduce and systematically evaluate three data analytics preprocessing methods to address these challenges. Results from evaluating actual cases of financial statement fraud suggest that two of these methods improve fraud prediction performance by approximately 10 percent relative to the best current techniques. Improved fraud prediction can result in meaningful benefits, such as improving the ability of the SEC to detect fraudulent filings and improving audit firms' client portfolio decisions.
Publisher
American Accounting Association
Subject
Economics and Econometrics,Finance,Accounting
Reference60 articles.
1. MetaFraud: A meta-learning framework for detecting financial fraud;Abbasi;MIS Quarterly,2012
2. Editorial—Big Data, data science, and analytics: The opportunity and challenge for IS research;Agarwal;Information Systems Research,2014
3. Forensic expert classification of management fraud risk factors;Apostolou;Journal of Forensic Accounting,2000
4. The relation between equity incentives and misreporting: The role of risk-taking incentives;Armstrong;Journal of Financial Economics,2013
5. Association of Certified Fraud Examiners (ACFE). 2014. Report to the Nation on Occupational Fraud and Abuse. Austin, TX: ACFE.
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