Персона:
Леонов, Павел Юрьевич

Организационные подразделения

Организационная единица

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.

Фамилия

Леонов

Имя

Павел Юрьевич

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Теперь показываю 1 - 8 из 8

Только метаданные
A Bayesian Network-Based Model for Fraud Risk Assessment
(2024) Leonov, P. Y.; Sushkov, V. M.; Stanislav V. Vishnevsky.; Romanovsky, V. A.; Леонов, Павел Юрьевич; Сушков, Виктор Михайлович; Романовский, Валентин Андреевич
Только метаданные
Testing 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
Только метаданные
Improving 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
Только метаданные
Integrated application of Benford's Law tests to detect corporate fraud
(2022) Leonov, P. Y.; Suyts, V. P.; Norkina, A. N.; Sushkov, V. M.; Леонов, Павел Юрьевич; Норкина, Анна Николаевна; Сушков, Виктор Михайлович
Только метаданные
Possibility of Benford’s Law Application for Diagnosing Inaccuracy of Financial Statements
(2022) Suyts, V. P.; Leonov, P. Y.; Rychkov, V. A.; Ezhova, A. A.; Sushkov, V. M.; Kuznetsova, N. V.; Леонов, Павел Юрьевич; Рычков, Вадим Александрович; Сушков, Виктор Михайлович; Кузнецова, Надежда Владимировна
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.The paper describes a technique for diagnosing data inaccuracy using Benford’s law. The Benford distribution for the first significant digit of a random decimal number is presented graphically and mathematically. The main requirements for data are listed, which are consistent with Benford’s law: the data must refer to one process, there must be no maximum and minimum restrictions in the studied population, artificial introduction of the numbering system is not allowed, and there must be no obvious linking patterns between numbers. When examining the possibility of applying Benford’s law to diagnose inaccuracies in the financial statements of an organization, the costs of two companies for payment of services to suppliers were analyzed. It was found that in the absence of attempts to manipulate reporting, performance indicators are close to theoretically predicted based on Benford’s law. Attempts to manipulate reporting are reflected in corresponding deviations from Benford’s law. The possibility of applying Benford’s law to diagnose unreliability of an organization’s financial statements has been proved.
Только метаданные
Factor Analysis-Based Model for Rating the National Financial Security
(2023) Leonov, P. Y.; Sushkov, V. M.; Elkina, D. Y.; Pavlov, D. S.; Yodgorov, S.; Krasinsky, V. V.; Леонов, Павел Юрьевич; Сушков, Виктор Михайлович; Елкина, Дарья Юрьевна; Павлов, Денис Сергеевич; Красинский, Владислав Вячеславович
Только метаданные
Detecting Money Laundering Patterns through Cash Flow Analysis: a Neural Network-Based Approach
(2023) Leonov, P. Y.; Sushkov, V. M.; Krasinsky, V. V.; Romanovsky, V. A.; Kuznetsova, N. V.; Akimov, N. V.; Леонов, Павел Юрьевич; Сушков, Виктор Михайлович; Красинский, Владислав Вячеславович; Романовский, Валентин Андреевич; Кузнецова, Надежда Владимировна
Только метаданные
Integrating 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 Юрьевич
Detecting 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 BenfordвЂ™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 BenfordвЂ™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.