1,720,952 research outputs found
Analysis of Possibilities to Automate Detection of Unscrupulous Microfinance Organizations based on Machine learning Methods
Get PDFMicrofinance is a way to fight poverty, and therefore is of high social significance. The microfinance sector in Russia is progressing. However, the engagement of microfinance organizations in illegal financial transactions associated with fraud, illegal creditors, money laundering, significantly limits their potential and has negative impact on their development. The aim of the paper is to study the possibilities to automate detection of unscrupulous microfinance organizations based on machine learning methods in order to promptly identify and suppress illegal activities by regulatory authorities. The author cites common fraudulent schemes involving microfinance organizations, including a scheme for cashing out maternity capital, a fraudulent lending scheme against real estate. The author carried out a comparative analysis of the results obtained by classification methods — the logistic regression method, decision trees (algorithms of two-class decision forest, Adaboost), support vector machine (algorithm of two-class support vector machine), neural network methods (algorithm of two-class neural network), Bayesian networks (algorithm of two-class Bayes network). The two-class support vector machine provided the most accurate results. The author analysed the data on microfinance institutions published by the Bank of Russia, the MFOs themselves, and banki.ru. The author concludes that the research results can be of further use by the Bank of Russia and Rosfinmonitoring to automate detection of unscrupulous microfinance organizations
Synthesis of Socio-Economic Maps and Visualization of Deviant Activity Measures of Financial Monitoring of Entities
Get PDFThe task analysis of the Federal Financial Monitoring Service has revealed that the money laundering risk assessment process is greatly limited by insufficient resources. The aim of the study is to increase the efficiency of decision-making processes by using visualization of financial monitoring data. The methodological basis of the study suggests to rank objects in order to map financial monitoring data. However, the objects of financial monitoring, such as business entities, professional securities market participants, have sets of characteristics, i.e. are of vector nature. As known, there is no mathematical definition of ordinal relations for vectors. The author used the method of principal component to estimate a scalar value of financial monitoring. The article provides a subject area modeling of financial monitoring, and the author used mathematical and methodological tools to map deviant objects of financial monitoring. The result of the study presents the geographical infographics of the money laundering process. The author refers to socio-economic regional maps obtained from various official sources (arbitration case files, the Unified State Register of Legal Entities, the crime rate in Russia from the Ministry of Internal Affairs). The maps include information about the business activity of the federal districts, regions with a propensity for illegal and legal financial activities, crime rate. The author concludes that the results of the study may serve as a powerful tool to support the strategic decision-making process and microanalysis of financial monitoring
Comparative Analysis of Machine learning Methods to Identify signs of suspicious Transactions of Credit Institutions and Their Clients
No full textIn the field of financial monitoring, it is necessary to promptly obtain objective assessments of economic entities (in particular, credit institutions) for effective decision-making. Automation of the process of identifying unscrupulous credit institutions based on machine learning methods will allow regulatory authorities to quickly identify and suppress illegal activities. The aim of the research is to substantiate the possibilities of using machine learning methods and algorithms for the automatic identification of unscrupulous credit institutions. It is required to select a mathematical toolkit for analyzing data on credit institutions, which allows tracking the involvement of a bank in money laundering processes. The paper provides a comparative analysis of the results of processing data on the activities of credit institutions using classification methods — logistic regression, decision trees. The author applies support vector machine and neural network methods, Bayesian networks (Two-Class Bayes Point Machine), and anomaly search — an algorithm of a One-Class Support Vector Machine and a PCA-Based Anomaly Detection algorithm. The study presents the results of solving the problem of classifying credit institutions in terms of possible involvement in money laundering processes, the results of analyzing data on the activities of credit institutions by methods of detecting anomalies. A comparative analysis of the results obtained using various modern algorithms for the classification and search for anomalies is carried out. The author concluded that the PCA-Based Anomaly Detection algorithm showed more accurate results compared to the One-Class Support Vector Machine algorithm. Of the considered classification algorithms, the most accurate results were shown by the Two-Class Boosted Decision Tree (AdaBoost) algorithm. The research results can be used by the Bank of Russia and Rosfinmonitoring to automate the identification of unscrupulous credit institution
Синтез социально-экономических карт и визуализация девиантной деятельности объектов финансового мониторинга
No full textThe task analysis of the Federal Financial Monitoring Service has revealed that the money laundering risk assessment process is greatly limited by insufficient resources. The aim of the study is to increase the efficiency of decision-making processes by using visualization of financial monitoring data. The methodological basis of the study suggests to rank objects in order to map financial monitoring data. However, the objects of financial monitoring, such as business entities, professional securities market participants, have sets of characteristics, i.e. are of vector nature. As known, there is no mathematical definition of ordinal relations for vectors. The author used the method of principal component to estimate a scalar value of financial monitoring. The article provides a subject area modeling of financial monitoring, and the author used mathematical and methodological tools to map deviant objects of financial monitoring. The result of the study presents the geographical infographics of the money laundering process. The author refers to socio-economic regional maps obtained from various official sources (arbitration case files, the Unified State Register of Legal Entities, the crime rate in Russia from the Ministry of Internal Affairs). The maps include information about the business activity of the federal districts, regions with a propensity for illegal and legal financial activities, crime rate. The author concludes that the results of the study may serve as a powerful tool to support the strategic decision-making process and microanalysis of financial monitoring.Анализ задач Росфинмониторинга по противодействию отмыванию доходов показал, что фактическая потребность в количестве объектов, подлежащих анализу, многократно превышает возможности аналитиков. Цель исследования состоит в повышении оперативности оценки обстановки лицами, принимающими решения, за счет визуализации данных финансового мониторинга. Методологическую основу исследования определил тот факт, что для картирования информации об объектах финансового мониторинга необходимо провести их ранжирование. Однако объекты финансового мониторинга - хозяйствующие субъекты, профессиональные участники рынка ценных бумаг - описывают наборами характеристик, т.е., по сути, являются объектами векторной природы. В математике же порядковые отношения для векторов, как известно, не определены. Для отыскания скалярных оценок объектов финансового мониторинга перспективным является метод главных компонент. Произведено моделирование предметной области финансового мониторинга и подобран математический и методологический инструментарий для решения задачи картирования девиантных объектов финансового мониторинга. Результатом моделирования является инфографика географической составляющей отмывания преступных доходов. На основе государственных данных из различных источников - картотеки арбитражных дел, единого государственного реестра юридических лиц, сведений о состоянии преступности МВД России - получены социально-экономические карты: бизнес-активности федеральных округов, федеральных округов по склонности предоставления теневых финансовых услуг, регионов по склонности к легализации денежных средств, состояния преступности. Автор делает вывод о том, что приведенные результаты исследования могут служить мощным инструментом поддержки принятия стратегических решений и макроанализа ситуации в сфере финансового мониторинга
Анализ возможностей автоматизации выявления недобросовестных микрофинансовых организаций на основе методов машинного обучения
Get PDFMicrofinance is a way to fight poverty, and therefore is of high social significance. The microfinance sector in Russia is progressing. However, the engagement of microfinance organizations in illegal financial transactions associated with fraud, illegal creditors, money laundering, significantly limits their potential and has negative impact on their development. The aim of the paper is to study the possibilities to automate detection of unscrupulous microfinance organizations based on machine learning methods in order to promptly identify and suppress illegal activities by regulatory authorities. The author cites common fraudulent schemes involving microfinance organizations, including a scheme for cashing out maternity capital, a fraudulent lending scheme against real estate. The author carried out a comparative analysis of the results obtained by classification methods — the logistic regression method, decision trees (algorithms of two-class decision forest, Adaboost), support vector machine (algorithm of two-class support vector machine), neural network methods (algorithm of two-class neural network), Bayesian networks (algorithm of two-class Bayes network). The two-class support vector machine provided the most accurate results. The author analysed the data on microfinance institutions published by the Bank of Russia, the MFOs themselves, and banki.ru. The author concludes that the research results can be of further use by the Bank of Russia and Rosfinmonitoring to automate detection of unscrupulous microfinance organizations.Микрофинансирование является одним из способов борьбы с бедностью, в связи с чем имеет высокую социальную значимость. Сфера микрофинансирования в России активно развивается. Но вовлеченность ми-крофинансовых организаций (МФО) в незаконные финансовые операции, связанные с мошенничеством, деятельностью нелегальных кредиторов, легализацией доходов, полученных преступным путем, существенно ограничивают их потенциал и негативно влияют на динамику развития. Цель исследования состоит в изучении возможностей автоматизации процесса выявления недобросовестных участников рынка микрофинансирования на основе методов и алгоритмов машинного обучения для оперативного выявления и пресечения противоправной деятельности контролирующими органами. Автор приводит распространенные мошеннические схемы с участием микрофинансовых организаций, в том числе схему обналичивания материнского капитала, мошенническую схему кредитования под залог недвижимости. Проведен сравнительный анализ результатов, полученных методами классификации — методом логистической регрессии, деревьев решений (алгоритмы двухклассовый лес решений, Adaboost), методом опорных векторов (алгоритм двухклассовая машина опорных векторов), нейросетевыми методами (алгоритм двухклассовой нейронной сети), Байесовскими сетями (алгоритм двухклассовой сети Байеса). Наиболее точные результаты показала двухклассовая машина опорных векторов. Анализ проведен на основе данных о микрофинансовых организациях, публикуемых Банком России, самими МФО, порталом banki.ru. Автор делает вывод о том, что приведенные результаты исследования могут быть использованы Банком России и Росфинмониторингом для автоматизации выявления недобросовестных микрофинансовых организаций
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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