Mining Financial Statement Fraud: An Analysis of Some Experimental Issues

TL;DR

This paper analyzes key experimental issues in financial statement fraud detection, emphasizing the importance of problem representation, feature selection, and performance metrics for effective algorithm implementation.

Contribution

It critically examines existing approaches and offers new insights into the experimental design considerations for financial fraud detection methods.

Findings

Highlights the impact of problem representation on detection accuracy

Discusses the significance of feature selection in fraud detection

Provides critique and new perspectives on performance metrics used

Abstract

Financial statement fraud detection is an important problem with a number of design aspects to consider. Issues such as (i) problem representation, (ii) feature selection, and (iii) choice of performance metrics all influence the perceived performance of detection algorithms. Efficient implementation of financial fraud detection methods relies on a clear understanding of these issues. In this paper we present an analysis of the three key experimental issues associated with financial statement fraud detection, critiquing the prevailing ideas and providing new understandings.