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Integrating Data Mining Techniques for Fraud Detection in Financial Control Processes

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dc.contributor.authorSushkov, V. M.
dc.contributor.authorLeonov, P. Y.
dc.contributor.authorNadezhina, O. S.
dc.contributor.authorBlagova, I. Y.
dc.contributor.authorСушков, Виктор Михайлович
dc.contributor.authorЛеонов, Павел Юрьевич
dc.date.accessioned2024-12-27T13:33:55Z
dc.date.available2024-12-27T13:33:55Z
dc.date.issued2023
dc.description.abstractDetecting fraud in financial control processes poses significant challenges due to the complex nature of financial transactions and the evolving tactics employed by fraudsters. This paper investigates the integration of data mining techniques, specifically the combination of Benford s Law and machine learning algorithms, to create an enhanced framework for fraud detection. The paper highlights the importance of combating fraudulent activities and the potential of data mining techniques to bolster detection efforts. The literature review explores existing methodologies and their limitations, emphasizing the suitability of Benford s Law for fraud detection. However, shortcomings in practical implementation necessitate improvements for its effective utilization in financial control. Consequently, the article proposes a methodology that combines informative statistical features revealed by Benford’s law tests and subsequent clustering to overcome its limitations. The results present findings from a financial audit conducted on a road-construction company, showcasing representations of primary, advanced, and associated Benford’s law tests. Additionally, by applying clustering techniques, a distinct class of suspicious transactions is successfully identified, highlighting the efficacy of the integrated approach. This class represents only a small proportion of the entire sample, thereby significantly reducing the labor costs of specialists for manual audit of transactions. In conclusion, this paper underscores the comprehensive understanding that can be achieved through the integration of Benford s Law and other data mining techniques in fraud detection, emphasizing their potential to automate and scale fraud detection efforts in financial control processes.
dc.format.extentС. 1675-1684
dc.identifier.citationIntegrating Data Mining Techniques for Fraud Detection in Financial Control Processes / Sushkov, V. M. [et al.] // International Journal of Technology. - 2023. - 14. - № 8. - P. 1675-1684. - 10.14716/ijtech.v14i8.6830
dc.identifier.doi10.14716/ijtech.v14i8.6830
dc.identifier.urihttps://www.doi.org/10.14716/ijtech.v14i8.6830
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85183723444&origin=resultslist
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/29638
dc.relation.ispartofInternational Journal of Technology
dc.subjectBenford s law
dc.subjectFraud Detection
dc.subjectaccounting data manipulation
dc.subjectfinancial data
dc.subjectforensic accounting
dc.titleIntegrating Data Mining Techniques for Fraud Detection in Financial Control Processes
dc.typeArticle
dspace.entity.typePublication
oaire.citation.issue8
oaire.citation.volume14
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