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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Теперь показываю 1 - 10 из 20

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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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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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.
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Application of machine learning methods for risk analysis of unfavorable outcome of government procurement procedure in building and grounds maintenance domain
(2021) Domashova, J.; Kripak, E.; Домашова, Дженни Владимировна
© 2020 Elsevier B.V.. All rights reserved.The article provides the results of applying machine learning methods for prediction of unfavorable outcome of the public procurement procedure in the building and grounds maintenance domain. Based on a comprehensive analysis of the domain it was decided to investigate the following risks: the risk of collusion among suppliers; the risk of conspiracy between customers and suppliers; the risk associated with inaccurate data in the Unified Information System. Usage of various classification techniques has been researched while modeling the problem in the domain. In order to form sustainable groups of suppliers, the association rule mining was done using the "Apriori" algorithm. While searching for representative characteristics of the groups of similar objects, the solution to the clustering problem was found using the Ward and K-means++ methods. The Cluster models, which were defined to analyze each of the collusion risks, were built on the feature space. The models make it possible to identify the most typical behavioral patterns of two suppliers with each other as well as the customer with the supplier.
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The Corruption Perception Index: Analysis of dependence on socio-economic indicators
(2021) Domashova, J.; Politova, A.; Домашова, Дженни Владимировна
© 2020 Elsevier B.V.. All rights reserved.The article presents the results of applying data analysis methods, in particular cluster analysis and machine learning methods for corruption analysis. The analysis of dependence of the corruption perception index on social and economic indicators in different countries of the world was carried out. Data was collected for the formation of the feature space, the most significant features that have the greatest impact on the corruption perception index were selected, and the countries of the world were clustered according to the selected features. A scheme for conducting a comprehensive analysis to identify the most significant signs and causes affecting the corruption perception index has been developed. Based on the results of clustering, a classification model that can predict the level of corruption in the country based on the values of selected attributes was trained. It is based on the composition of Bagging algorithms with decision trees as basic classifiers. The results of the study can be used by public authorities to develop, organize and adopt appropriate counteraction measures. A software tool based on in the Python language, which allows to perform appropriate analysis using updated data was developed.
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Detection of fraudulent transactions using SAS Viya machine learning algorithms
(2021) Domashova, J.; Zabelina, O.; Домашова, Дженни Владимировна
© 2020 Elsevier B.V.. All rights reserved.The article presents the results of applying machine learning techniques to detect fraudulent banking transactions. The market of antifraud systems was studied. Ensemble methods for solving classification problem as well as dimensionality reduction techniques were examined. The proposed analysis procedure is based on the selection of the best machine learning model and the identification of the most significant features for detecting fraud. Results-based recommendations can be used in financial institutions as well as in other organizations, where it is required to identify and prevent entities' fraudulent actions that pose a threat to the functioning of business processes and electronic systems. The proposed fraud detection methodology was implemented on the cloud-based analytical platform Statistical Analysis System (SAS) Viya.
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Detection of non-typical users of the electronic marketplace "freight transportation" to prevent the competitive intelligence
(2021) Domashova, J.; Zasypkina, A.; Домашова, Дженни Владимировна
© 2020 Elsevier B.V.. All rights reserved.This article presents results of usage of cluster analysis and machine learning to detect non-typical users of the electronic freight transportation marketplace. The most significant red flags to identify system users who collect commercial information have been identified, a competitor's profile has been drawn up. The use of various methods of clustering and classification in the modeling of this problem is studied. The procedure of formation of a training sample in the absence of labeled data has been carried out. The algorithm has been developed to detect non-typical users based on machine data analysis methods in order to prevent the real-time commercial intelligence. This algorithm is based on density-based spatial clustering of applications with noise (DBSCAN) and balanced iterative reducing and clustering using hierarchies (BIRCH) cluster analysis methods, identification of anomalous objects using the Isolation Forest method and ensemble Gradient Boosting algorithm with decision trees as the base classifiers. It can be used by electronic marketplace's owners, as well as by other companies that apply similar technologies while providing their services. As a result, software tool in Python has been developed, which allows to timely detect users collecting commercial information and hence to reduce a negative impact from possible competitive intelligence.
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.
Только метаданные
Detection of Fraudulent Financial Transactions Using Autoencoder and Semantic Embeddings
(2026) Leonov, P. Y.; Domashova, J. V.; Sushkov, V. M.; Fazly, T. G.; Леонов, Павел Юрьевич; Домашова, Дженни Владимировна; Сушков, Виктор Михайлович; Фазлы, Тимофей Геннадьевич
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Constructor of compositions of machine learning models for solving classification problems
(2020) Lavrov, I.; Domashova, J.; Домашова, Дженни Владимировна
© 2020 The Authors. Published by Elsevier B.V.The article concerns the description of program software that gives an opportunity to solve classification problem by machine learning methods on input data in context of different subject areas. The list of such tasks can include: Identification of suspicious state contracts for collusion of suppliers, forecasting the execution of a government contract, forecasting license revocation from credit organizations and insurance companies, etc. The application allows a user to build a composition of several base machine learning models to solve classification problems. As part of the study, the functional requirements for the product being created are presented, the architecture has been developed, the design and testing of the proposed process for creating machine learning model compositions has been carried out. The use of the application will allow to build compositions of machine learning models in a user-friendly mode in order to increase the accuracy of the classification problem. The use of the proposed tools improves the accuracy of solving the classification problem on input data in the context of various subject areas through the utilization of a composition of machine learning methods.