ooОткрытое образованиеOpen Education1818-42432079-5939Plekhanov Russian University of Economics10.21686/1818-4243-2026-3-23-35oo-1326Research ArticleНОВЫЕ ТЕХНОЛОГИИNEW TECHNOLOGIESВопросы разработки интеллектуальной системы превентивного мониторинга наступления событий операционных рисковIssues of Developing an Intelligent System for Preventive Monitoring of the Occurrence of Operational Risk EventsЧумаковаЕ. В.ChumakovaE. V.

Екатерина Витальевна Чумакова, К.ф.-м.н., доцент, доцент кафедры прикладной информатики и информационной безопасности

Москва

Ekaterina V. Chumakova, Cand. Sci. (Physics and Mathematics), Associate Professor, Associate Professor of the Department of Applied Informatics and Information Security

Moscow

catarinach@yandex.ruКорнеевД. Г.KorneevD. G.

Дмитрий Геннадьевич Корнеев, К.э.н., доцент, доцент кафедры прикладной информатики и информационной безопасности

Москва

Dmitry G. Korneev, Cand. Sci. (Economics), Associate Professor, Associate Professor of the Department of Applied Informatics and Information Security

Moscow

Korneev.DG@rea.ruГаспарианМ. С.GasparianM. S.

Михаил Самуилович Гаспариан, К.э.н., доцент, доцент кафедры прикладной информатики и информационной безопасности

Москва

Mikhail S. Gasparian, Cand. Sci. (Economics),, Associate Professor, Associate Professor of the Department of Applied Informatics and Information Security

Moscow

Gasparian.MS@rea.ruПономаревА. А.PonomarevA. A.

Андрей Александрович Пономарев, Старший преподаватель кафедры прикладной информатики и информационной безопасности

Москва

Andrey A. Ponomarev, Senior Lecturer at the Department of Applied Informatics and Information Security

Moscow

ponomarev.AA@rea.ruРоссийский экономический университет им. Г.В. ПлехановаРоссияPlekhanov Russian University of EconomicsRussian Federation2026010820263032335Copyright © Чумакова Е.В., Корнеев Д.Г., Гаспариан М.С., Пономарев А.А., 20262026Чумакова Е.В., Корнеев Д.Г., Гаспариан М.С., Пономарев А.А.Chumakova E.V., Korneev D.G., Gasparian M.S., Ponomarev A.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://openedu.rea.ru/jour/article/view/1326

В рамках управления операционными рисками бизнес-процессы рассматриваются в качестве одного из генераторов рисковых событий. Эмпирические данные свидетельствуют, что существенная доля операционных потерь в кредитных организациях связана с человеческим фактором, а именно с профессиональной неподготовленностью сотрудников. В этом контексте, перспективным направлением риск-менеджмента является разработка и внедрение инструментов на основе искусственного интеллекта, предназначенных для автоматизированной оценки уровня критичности операций, что позволяет нивелировать риски, порождаемые персоналом. 

Целью работы является разработка интеллектуальной системы превентивного мониторинга наступления критического состояния бизнес-процесса вследствие действий или бездействий персонала для предотвращения события операционного риска. Для этого были проанализированы профессиональные и личностные критерии оценки персонала, критерии оценки их влияния на бизнес-процесс, а также накопленные статистические показатели. Предложена общая структура системы индикации состояния бизнес-процесса, организованная по модульному принципу. В качестве составных модулей предложено использовать искусственные нейронные сети (ИНС) прямого распространения, описаны основные потоки данных, поступающие на входы ИНС. Проведено сравнение различных моделей ИНС для каждого из модулей системы. Полученные результаты могут быть использованы в различных сферах деятельности, связанных с действиями персонала, для предотвращения негативных последствий наступления критического состояния бизнес-процесса.

As part of operational risk management, business processes are considered as one of the generators of risk events. Empirical data show that a significant share of operational losses in credit institutions is associated with the human factor, namely, the professional unpreparedness of employees. In this context, a promising area of risk management is the development and implementation of tools based on artificial intelligence, designed for automated assessment of the criticality level of operations, which allows mitigating the risks generated by personnel.

The aim of the paper is to develop an intelligent system for preventive monitoring of the occurrence of a critical state of a business process due to actions or omissions of personnel to prevent an operational risk event.

To achieve this goal, professional and personal criteria for evaluating personnel, criteria for assessing their impact on the business process, as well as accumulated statistical indexes were analyzed. The general structure of the business process status indication system is proposed, organized according to the modular principle. It is proposed to use artificial neural networks (ANN) of direct propagation as composite modules. The paper describes the main data flows coming to the ANN inputs and compares different ANN models for each of the system modules.

The results obtained can be used in various areas of activity related to personnel actions to prevent the negative consequences of the critical state of the business process.

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The authors declare that there are no conflicts of interest present.