Персона: Леонов, Павел Юрьевич
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Институт финансовых технологий и экономической безопасности
Институт финансовых технологий и экономической безопасности (ИФТЭБ) Национального исследовательского ядерного университета "МИФИ" готовит кадры в интересах национальной системы по противодействию легализации (отмыванию) доходов, полученных преступным путем, и финансированию терроризма (ПОД/ФТ).
Междисциплинарность образования позволит выпускникам ИФТЭБ НИЯУ МИФИ легко адаптироваться на современном рынке труда и в бизнес-среде.
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- ПубликацияТолько метаданныеTesting for Benford’s Law as a Response to the Risks of Material Misstatement Due to Fraud(2024) Sushkov, V. M.; Leonov, P. Y.; Сушков, Виктор Михайлович; Леонов, Павел Юрьевич
- ПубликацияТолько метаданныеImproving the Methodology for Integrated Testing of Journal Entries by Benford’s Law(2024) Leonov, P. Y.; Sushkov, V. M.; Boiko, S. A.; Stepanenkova, M. A.; Леонов, Павел Юрьевич; Сушков, Виктор Михайлович
- ПубликацияТолько метаданныеIntegrating Data Mining Techniques for Fraud Detection in Financial Control Processes(2023) Sushkov, V. M.; Leonov, P. Y.; Nadezhina, O. S.; Blagova, I. Y.; Сушков, Виктор Михайлович; Леонов, Павел ЮрьевичDetecting 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.