Fraudulent Firm Classification: A Case Study of an External Audit

This paper is a case study of visiting an external audit company to explore the usefulness of machine learning algorithms for improving the quality of an audit work. Annual data of 777 firms from 14 different sectors are collected. The Particle Swarm Optimization (PSO) algorithm is used as a feature...

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Bibliographic Details
Published in:Applied artificial intelligence 2018-01, Vol.32 (1), p.48-64
Main Authors: Hooda, Nishtha, Bawa, Seema, Rana, Prashant Singh
Format: Article
Language:English
Subjects:
Artificial intelligence
Audits
Bayesian analysis
Case studies
Classification
Correlation analysis
Correlation coefficients
Decision making
Error analysis
Error detection
Fraud
Machine learning
Particle swarm optimization
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Summary:This paper is a case study of visiting an external audit company to explore the usefulness of machine learning algorithms for improving the quality of an audit work. Annual data of 777 firms from 14 different sectors are collected. The Particle Swarm Optimization (PSO) algorithm is used as a feature selection method. Ten different state-of-the-art classification models are compared in terms of their accuracy, error rate, sensitivity, specificity, F measures, Mathew's Correlation Coefficient (MCC), Type-I error, Type-II error, and Area Under the Curve (AUC) using Multi-Criteria Decision-Making methods like Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results of Bayes Net and J48 demonstrate an accuracy of 93% for suspicious firm classification. With the appearance of tremendous growth of financial fraud cases, machine learning will play a big part in improving the quality of an audit field work in the future.
ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2018.1451032