Персона:
Домашова, Дженни Владимировна

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.

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Домашова

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Дженни Владимировна

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ИНФОРМАЦИОННО-АНАЛИТИЧЕСКАЯ СИСТЕМА РЕШЕНИЯ ЗАДАЧ МАШИННОГО ОБУЧЕНИЯ С УЧИТЕЛЕМ
(НИЯУ МИФИ, 2023) Домашова, Д. В.; Гуляев, Е. А.; Косвинцев, К. Е.; Скляр, Д. М.; Кольца, И. В.; Калугер, Р. С.; Гузев, А. Е.; Белозерова, А. А.; Домашова, Дженни Владимировна
Программа позволяет построить композицию произвольных моделей машинного обучения для определения классов заданных объектов. Программа поддерживает следующие этапы проведения анализа: предварительный анализ исходных данных пользователя, предобработку данных, отбор информативных признаков, построение композиций моделей машинного обучения, их обучение, представление характеристик результатов обучения с целью анализа полученного качества предсказания, а также применение обученных моделей для классификации. На каждом этапе пользователю предоставляется возможность выбора конкретных методов из доступного перечня алгоритмов обработки. Построенные и/или обученные композиции моделей могут быть сохранены для дальнейшего использования. ОС: Windows.
Только метаданные
Application of machine learning methods to identify suspicious actions of employees related to violation of the procedures of a credit institution
(2022) Domashova, J.; Kripak, E.; Pisarchik, E.; Домашова, Дженни Владимировна
The article presents the results of the application of machine learning methods to identify suspicious actions of employees related to a violation of the procedures of a credit institution, specifically, the theft of funds from customer accounts and cards and abuse of the motivation system. The stages of data preprocessing within the considered task are analyzed. Among the considered classification algorithms, which are not sensitive to class imbalance, the method with the best value of hyperparameters was chosen. Next, the most informative features were highlighted, for which the best values of hyperparameters were selected and the optimal values of the probability thresholds of attributing an object to fraud were found. The proposed technology can be used separately or as part of an anti-fraud system for routine (for example, once a month) detection of illegitimate actions of employees of a credit institution related to the theft of funds from customer accounts and cards and abuse of the motivation system. A software tool in Python was developed that allows solving the task of detecting internal fraud based on the proposed technology.
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Identification of atypical behavior of bank employees when using e-mail to prevent information leakages
(2022) Domashova, J.; Bystrova, E.; Домашова, Дженни Владимировна
The article presents the results of using machine learning methods to identify atypical behavior of bank employees when using e-mail. A feature space is formed that characterizes the behavior of e-mail users. The objects were previously clustered using the density-based spatial clustering of applications with noise (DBSCAN) and the fuzzy logic elements. The objects were marked using the inbuilt business rules, and the training sample was formed in the absence of marked data. The most informative features are selected, and a model of classification of e-mail users by the type of their behavior is constructed. A feature space is formed that defines the characteristics of a particular message to identify messages that are the information security incidents. Preliminary data processing was performed by removing the duplicates and encoding the categorical variables. A model of messages classification is constructed. The best combination of the machine learning method and the feature selection algorithm was determined using quality metrics. The constructed models allow specialists of cybersecurity departments of banks to identify employees with abnormal behavior and possibly involved in information leaks. A software tool in Python was developed that makes it easier to identify the final status of a message by partially replacing its manual detection for an automatic one.
Только метаданные
Machine learning Models Compositions Builder
(2022) Domashova, J.; Norkina, A.; Kosvintsev, K.; Belozerova, A.; Alexey Guzev.; Домашова, Дженни Владимировна; Норкина, Анна Николаевна
Только метаданные
Development of a generalized algorithm for identifying atypical bank transactions using machine learning methods
(2022) Domashova, J.; Kripak, E.; Домашова, Дженни Владимировна
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Detection and analysis of atypical stock transactions with possible misuse of insider information and market manipulation: Methods and models
(2022) Domashova, J.; Yakimov, D.; Bredikhin, D.; Gorbunov, K.; Slavik, R.; Kadyrov, I.; Домашова, Дженни Владимировна
The article presents the results of the application of machine learning methods to identify customers prone to the use of insider information and market manipulation. The feature space was formed taking into account the specifics of the subject area. Cluster analysis techniques were used to markup data and to construct a training sample to identify features of anomalous objects. The key distinguishing features of atypical transactions were obtained and analyzed, an atypical investor profile was created. Ensemble algorithms of machine learning were used to build models for identifying transactions with possible misuse of insider information and signs of market manipulation. The practical significance of the study lies in the fact that the use of models will allow to effectively and timely identify atypical transactions, as well as unscrupulous investors involved in the misuse of insider information and market manipulation. The proposed approaches can be applied both in the work of the Bank of Russia and in the brokers work, who should independently monitor the doubtful operations of their clients.
Только метаданные
Developing a named entity recognition model for text documents in Russian to detect personal data using machine learning methods
(2022) Gultiaev, A. A.; Domashova, J.; Домашова, Дженни Владимировна
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Usage of machine learning methods for early detection of money laundering schemes
(2021) Domashova, J.; Mikhailina, N.; Домашова, Дженни Владимировна
© 2020 Elsevier B.V.. All rights reserved.The article presents the results of applying machine learning methods to identify organizations prone to money laundering. Methods of data preprocessing were analyzed: categorical features encoding, informative feature selection. Classification methods were studied, in particular, ensemble methods of machine learning, algorithms for selecting the optimal hyperparameters, and methods for assessing the quality of the model. The most significant anti-money laundering and combating the financing of terrorism (AML/CFT) features in suspicious organizations when opening a current account were determined. The use of combinations of different methods of transformation of categorical features with the type of cross-validation used in modeling of the mentioned task was explored. The expediency of using TargetEncoder with double cross-validation was demonstrated. A model for identifying organizations prone to money laundering is trained. The best prediction quality is achieved by using a gradient boosting algorithm over decision trees. The quality of hyperparameter selection using hyperopt and optuna Python libraries was studied, and the speed of obtaining the optimal set was estimated. The model can be used to form a list of the most important indicators for early detection of organizations involved in money laundering and terrorist financing (ML/TF), as well as to develop adequate recommendations to improve the compliance control process. A software tool in Python was developed that allows to solve the tasks of early detection of organizations prone to money laundering.
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Predicting the revocation of a bank license using machine learning algorithms
(2021) Domashova, J. V.; Gultiaev, A. A.; Домашова, Дженни Владимировна
© 2020 Elsevier B.V.. All rights reserved.This article presents the results of applying various machine learning methods to predict the revocation of credit organizations' licenses in Russia. The goal of the research is to predict whether the bank's license will be revoked soon. The feature space was analyzed, and additional features were calculated. Different basic classification algorithms, such as logistic regression, support vector machines classifier, decision tree, and bagging ensemble algorithm, were tested to solve the problem. Each algorithm was optimized to perform well with a highly unbalanced data. An enhanced bagging-based algorithm with weighted voting was developed to improve the classification quality. The results of this research can be used both by credit organizations themselves to monitor business conditions and assess risks, and by legal entities that cooperate with them for safe placement of their funds. A software tool in Python that allows solving problems of timely prediction of license revocation based on the developed algorithm was developed.
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Selecting an optimal architecture of neural network using genetic algorithm
(2021) Domashova, J. V.; Emtseva, S. S.; Fail, V. S.; Gridin, A. S.; Домашова, Дженни Владимировна; Гридин, Александр Сергеевич
© 2020 Elsevier B.V.. All rights reserved.The article presents the results of applying a genetic algorithm to find the most optimal architecture of the neural network that would solve classification problem with minimal errors. The stages of the genetic algorithm are considered and the rule for encoding the parameters of the neural network is determined. A genetic algorithm for constructing the optimal architecture of a multilayer perceptron for solving classification problems has been developed. The algorithm independently creates a random population, evolves, creating new generations with more adapted individuals, i.e., neural networks with better architectures than previous generations. The paper describes the process of population formation, substantiates the choice of the fitness function and the method of selecting parents. Modifications of the crossover and mutation operators are proposed in order to ensure the operability of the algorithm on variable size individuals. A software tool that generates a neural network with the best parameters for solving classification problems has been developed on Python3 programming language. The architecture of a neural network for detecting fraudulent transactions has been built by using the developed software.