Interpretable Fuzzy Rule-Based Systems for Detecting Financial Statement Fraud

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F19%3A39914916" target="_blank" >RIV/00216275:25410/19:39914916 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-19823-7_36" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-19823-7_36</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-19823-7_36" target="_blank" >10.1007/978-3-030-19823-7_36</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Interpretable Fuzzy Rule-Based Systems for Detecting Financial Statement Fraud

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Interpretable Fuzzy Rule-Based Systems for Detecting Financial Statement Fraud

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA19-15498S" target="_blank" >GA19-15498S: Modelování emocí ve verbální a neverbální manažerské komunikaci pro predikci podnikových finančních rizik</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IFIP Advances in Information and Communication Technology. Vol. 559

ISBN

978-3-030-19822-0

ISSN

1868-4238

e-ISSN

—

Počet stran výsledku

12

Strana od-do

425-436

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Hersonissos

Datum konání akce

24. 5. 2019

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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