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Online social networking as tool for risk modeling scams in express crediting

Mastyaeva I.N. PhD of Technical Sciences, Associate Professor, Head of the Department of Applied Mathematics, the Moscow State University of Economics, Statistics and Informatics ( imastyaeva@mesi.ru )

Snegova A.G. Chief Specialist of the Department for Retail Business of the Credit Risk of "MTS-BANK", JSC ( snegovalena@yandex.ru )

Journal: Economic Analysis: Theory and Practice, #24, 2013

To the banks issuing express loans, it is important to be able to operate arising risks for minimization of inevitable losses. Authors consider the methods of control over risks of internal and external fraud in express crediting. As the instrument of modeling of risks of fraud it is offered to use networks of social communications of borrowers.


Simulation of fuzzy-logic control system based on results of customs activity

Goremykina G.I. PhD of Physics and Mathematics, Associate Professor, the Department of Applied Mathematics, the Moscow State University of Economics, Statistics and Informatics ( g_iv.05e @ mail.ru )

Mastyaeva I.N. PhD of Technical Sciences, Associate Professor, Head of the Department of Applied Mathematics, the Moscow State University of Economics, Statistics and Informatics ( imastyaeva@mesi.ru )

Journal: National Interests: Priorities and Security, #23, 2013

In the article the model of fuzzy logic control system according to the results of the customs authorities on the basis of key performance indicators is presented. In model the scheme of an indistinct conclusion across Mamdani according to expert indistinct knowledge bases is applied. Process of system development is realized in the environment of MatLab with use of a Fuzzy Logic Toolbox package and the interactive fuzzy module. Some practical recommendations on how to build this system, modeling of the system parameters are offered.


Modeling the Decision Support System in management of investment projects for prevention of emergency situations

Goremykina G.I. Plekhanov Russian University of Economics, Moscow, Russian Federation ( g_iv.05@mail.ru )

Konstantinova O.V. Plekhanov Russian University of Economics, Moscow, Russian Federation ( konstantinova93@yandex.ru )

Mastyaeva I.N. Plekhanov Russian University of Economics, Moscow, Russian Federation ( imastyaeva@mail.ru )

Journal: National Interests: Priorities and Security, #3, 2017

Importance A new development strategy of the EMERCOM of Russia requires a new approach set up by 2030 to shift from operational response towards risk management, prevention and elimination of large hazardous factors, risks and threats. Active investment policies of the State is one of the requirements for successful implementation of governmental programs. Growing volume of investment, formation and implementation of the effective strategy of investment development require to create and integrate scientifically proven investment management models and methods in the specific operations of the EMERCOM of Russia.
Objectives The research creates and presents computer-assisted representation of the mathematical model for systemic support of decision-making in management of investment projects for emergency prevention.
Methods The research involves the fuzzy modeling methodology.
Results We set the Decision Support System model in investment project management for prevention of emergency situations. The model is based on the fuzzy inference scheme under the Mamdani algorithm. The system has been devised in MathLab using the Fuzzy Logic Toolbox package. We performed a comparative analysis of the models, investment projects on the basis of the system and prioritized their finance.
Conclusions and Relevance The research has practical significance since the system can be used as a versatile method to manage investment projects for emergency prevention.


Modeling the operational risk assessment of retail banking business line in express lending

Goremykina G.I. Plekhanov Russian University of Economics, Moscow, Russian Federation ( g_iv.05@mail.ru )

Shchukina N.A. Plekhanov Russian University of Economics, Moscow, Russian Federation ( shchukinan@yandex.ru )

Mastyaeva I.N. Plekhanov Russian University of Economics, Moscow, Russian Federation ( imastyaeva@mail.ru )

Journal: Finance and Credit, #12, 2018

Subject The upward trend in instant loans necessitates formalized tools to simulate and analyze the Retail Banking business line in express lending. The operational risk assessment is one of the main modeling objects of the said business line.
Objectives We aim to develop an aggregated model to assess risks in instant lending that correspond to two out of seven event type categories of operational risk associated with default on loans, which were standardized by the Basel Committee on Banking Supervision.
Methods The study draws on the simulation modeling methodology.
Results We built an aggregated economic and mathematical model to assess risks in express lending. The model considers the operational risk associated with opportunistic behavior by agents. Using the model, we developed a methodology and algorithm for assessment. The paper also provides a classification of retail outlets that takes into account the potential for opportunistic behavior by agents.
Conclusions Our simulation observations enable an aggregate assessment of external and internal operational risks inherent in the Retail Banking business line of banking outlets in the current period. The findings may be applied by banks and microlenders to assess operational risks in instant lending and create a system to manage them.


Modeling the risk management system in power-generating sector companies

Goremykina G.I. Moscow State University of Economics, Statistics and Informatics, Moscow, Russian Federation ( g_iv.05@mail.ru )

Mastyaeva I.N. Moscow State University of Economics, Statistics and Informatics, Moscow, Russian Federation ( imastyaeva@mesi.ru )

Fedorchuk A.A. Autonomous Non-commercial Organization UFL Organizing Committee, Moscow, Russian Federation ( anna.fedorchuk.86@mail.ru )

Journal: National Interests: Priorities and Security, #5, 2015

Importance The target-oriented and forecasting fuel and energy balance of Russia for the period until 2035 presumes advanced development of the electric-power industry for the realization of large-scale electrification of the national economy with the growth of installed power in power plants by more than 1/3 times increase and 1.6 times increase of generation of electricity. The change of functioning conditions directly impacts each electric-power industry facility. Because of this, at present, Russia is experiencing reforming of electric-power industry: of wholesale market liberalization of electric energy, implementation of the energy-saving and energy-efficiency programs, changing of tariff regulation and creation of wholesale market of power capacities. As a result, the organizations need new tools and technologies for transforming regulation market into competition market. A competition market is characterized by decision-making under condition of uncertainty. As a result, there is a need of forecasting of potential loses, and it means creating risk-management system.
     Objectives The aim of this research is the development of risk estimation model of electric energy company for constructing optimal strategy for market behavior. For achieving this goal, there were some tasks which were set up and solved, namely: different approaches to modeling risk-management system depending on quantity and quality of input data have been analyzed and compared; fuzzy-logic model of risk-management system of an electric-power company based upon key indicators and developed modeling of its parameters.
     Methods In this research, we have developed fuzzy-modeling methodology of evaluation and risks management of an electric-power company.
     Results We have constructed a model of risk management system. In the proposed model, we have used the Mamdani-Type Fuzzy Inference according to expert fuzzy knowledge basis. The development process of the system is implemented in MatLab environment using the Fuzzy Logic Toolbox package. The paper offered practical recommendations concerning construction methods of the mentioned system, and also carried out its parameters modeling.
     Conclusions and Relevance The practical significance of the research lies in the opportunity of applying the developed system as universal tool for assessing risks and creating a set of measures to minimize it.


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