Abstract

Systems for detecting financial statement frauds have attracted considerable interest in computational intelligence research. Diverse classification methods have been employed to perform automatic detection of fraudulent companies. However, previous research has aimed to develop highly accurate detection systems, while neglecting the interpretability of those systems. Here we propose a novel fuzzy rule-based detection system that integrates a feature selection component and rule extraction to achieve a highly interpretable system in terms of rule complexity and granularity. Specifically, we use a genetic feature selection to remove irrelevant attributes and then we perform a comparative analysis of state-of-the-art fuzzy rule-based systems, including FURIA and evolutionary fuzzy rule-based systems. Here, we show that using such systems leads not only to competitive accuracy but also to desirable interpretability. This finding has important implications for auditors and other users of the detection systems of financial statement fraud.

Highlights

  • Corruption and asset misappropriation tend to occur at greater frequency, financial statement fraud is reported to be the most costly internal fraud with a median loss of 800,000 USD at global level [1]

  • The results showed that probabilistic neural networks performed best, while the performance of evolutionary algorithms was significantly improved when applying feature selection first

  • The results suggest that the evolutionary fuzzy rule-based systems using the Michigan learning was not effective, whereas the gradual generation of the rule base as applied in the iterative learning is a more effective strategy

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Summary

Introduction

Corruption and asset misappropriation tend to occur at greater frequency, financial statement fraud is reported to be the most costly internal (occupational) fraud with a median loss of 800,000 USD at global level [1]. Note that this value is based on the estimate of the gross amount of the financial statement misstatement. Fraud incidents have adverse impact on fraudulent companies’ value, often resulting in filing for bankruptcy. Detection of financial statement fraud is of eminent importance for companies’ stakeholders.

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