Articlebronze2018

Fraudulent Firm Classification: A Case Study of an External Audit

Nishtha Hooda · Seema Bawa · Prashant Singh Rana

67 citations0/0 files storedApplied Artificial Intelligence

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Financial Distress and Bankruptcy Prediction Corporate Governance and Financial Management Imbalanced Data Classification Techniques

Abstract

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.

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References

2000Multi-criteria Decision Making Methods: A Comparative Study 2009Methodologies for data quality assessment and improvement 2010Detection of financial statement fraud and feature selection using data mining techniques 2002Detecting false financial statements using published data: some evidence from Greece 1999The Audit Risk Model, Business Risk and Audit-Planning Decisions 1997Assessing the Risk of Management Fraud Through Neural Network Technology.1999The Audit Risk Model, Business Risk, and Audit Planning Decisions 2008Belief-Function Formulas for Audit Risk

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