Detecting evolutionary financial statement fraud

Authors:
Wei Zhou;Gaurav Kapoor
Affiliations:
Information Systems and Technologies, ESCP Europe, 75543 Paris cedex 11, France;Information Systems and Operations Management, University of Florida, Gainesville, Florida 32611, USA
Venue:
Decision Support Systems
Year:
2011

Citing 5
Cited 1

A generalized qualitative-response model and the analysis of management fraud

Management Science
Detection of Management Fraud: A Neural Network Approach

CAIA '95 Proceedings of the 11th Conference on Artificial Intelligence for Applications
Data Mining techniques for the detection of fraudulent financial statements

Expert Systems with Applications: An International Journal
Review:

Neural Computation
Using neural networks and data mining techniques for the financial distress prediction model

Expert Systems with Applications: An International Journal

The impact of multinationality on firm value: A comparative analysis of machine learning techniques

Decision Support Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

A fraudulent financial statement involves the intentional furnishing and/or publishing of false information in it and this has become a severe economic and social problem. We consider Data Mining (DM) based financial fraud detection techniques (such as regression, decision tree, neural networks and Bayesian networks) that help identify fraud. The effectiveness of these DM methods (and their limitations) is examined, especially when new schemes of financial statement fraud adapt to the detection techniques. We then explore a self-adaptive framework (based on a response surface model) with domain knowledge to detect financial statement fraud. We conclude by suggesting that, in an era with evolutionary financial frauds, computer assisted automated fraud detection mechanisms will be more effective and efficient with specialized domain knowledge.