Interpretable Fuzzy Rule-Based Systems for Detecting Financial Statement Fraud

By Petr Hájek. 2019

19 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

InterpretML: A Unified Framework for Machine Learning Interpretability (2019, background): “, 2015), finance (Hajek, 2019; Chen et al., 2018) and judicial environments (Tan et al.”