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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 18

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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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Identification of non-typical international transactions on bank cards of individuals using machine learning methods
(2021) Domashova, J.; Kripak, E.; Домашова, Дженни Владимировна
© 2020 Elsevier B.V.. All rights reserved.The growing popularity of payment cards has led to the emergence of new types of illegal transactions with money. In particular, the widespread use of non-cash payments has allowed fraud to reach the international level. Therefore, financial institutions are interested in the development and implementation of new effective fraud monitoring systems that will minimize the risk of approving illegal transactions. The article presents the results of applying machine learning methods to detect fraudulent transactions with bank cards. The use of various classification methods in modeling the specified problem is investigated. Generalized algorithm for detecting fraudulent transactions has been developed, which makes it possible to detect atypical international money transfers in real time. Generalized algorithm for detecting atypical international transfers will allow timely detection of potential fraud cases, thereby reducing the total volume of losses from illegal transactions and minimizing the reputation damage caused to the organization.
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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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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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Technology of forecasting potentially unstable credit organizations based on machine learning methods
(2020) Domashova, J.; Kulaev, M.; Домашова, Дженни Владимировна
© 2020 The Authors. Published by Elsevier B.V.The article presents the results of the application of machine learning methods, in particular, various modifications of decision trees, to predict potentially unstable credit organizations. The application of different modifications of decision trees in the modeling of the specified task and current situation in banking sphere are considered. The technology for solving classification problems using machine learning methods is generalized. A Python program script, which enables to solve classification problems on the basis of the proposed methodology, was developed. The results of the application of machine learning methods using the developed program to solve this problem were described and their quality was analyzed.
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Identification of public procurement contracts with a high risk of non-performance based on neural networks
(2020) Ovsyannikova, A.; Domashova, J.; Домашова, Дженни Владимировна
© 2020 The Authors. Published by Elsevier B.V.The article is devoted to the use of machine learning methods, neural networks in particular, to predict the results of the execution of public procurement contracts. In the framework of the study, a classification of public contracts in pipe industry was carried out in order to identify contracts with high risk of non-performance, a Python application had been developed for the solution of the problem, which implemented compositions of neural network models of the architecture proposed in this work. The results of mathematical models application are described and their accuracy is analyzed. It is noted that the developed software product can be used for solving the described classification problem using neural network models with proper selection of training data.
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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.
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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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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.