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ISSN 2227-6017 (ONLINE), ISSN 2303-9868 (PRINT), DOI: 10.18454/IRJ.2227-6017
ПИ № ФС 77 - 51217, 16+

Скачать PDF ( ) Страницы: 70-71 Выпуск: №5 (24) Часть 2 () Искать в Google Scholar
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Федоров Б. М. ПРИМЕНЕНИЕ ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ ПРИ УПРАВЛЕНИИ РИСКОМ ЛИКВИДНОСТИ БАНКА / Б. М. Федоров // Международный научно-исследовательский журнал. — 2020. — №5 (24) Часть 2. — С. 70—71. — URL: https://research-journal.org/economical/use-of-information-technologies-in-managemen-of-liquidity-risk-in-a-bank/ (дата обращения: 31.03.2020. ).
Федоров Б. М. ПРИМЕНЕНИЕ ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ ПРИ УПРАВЛЕНИИ РИСКОМ ЛИКВИДНОСТИ БАНКА / Б. М. Федоров // Международный научно-исследовательский журнал. — 2020. — №5 (24) Часть 2. — С. 70—71.

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ПРИМЕНЕНИЕ ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ ПРИ УПРАВЛЕНИИ РИСКОМ ЛИКВИДНОСТИ БАНКА

Федоров Б.М.

Аспирант, Московский государственный университет экономики, статистики и информатики (МЭСИ)

ПРИМЕНЕНИЕ ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ ПРИ УПРАВЛЕНИИ РИСКОМ ЛИКВИДНОСТИ БАНКА

Аннотация

Риск ликвидности банка является одним из важнейших рисков. В статье рассмотрено. В статье описываются требования к информационной системе для управления риском ликвидности. Автор описывает архитектурные особенности системы, включая конкретные механизмы.

Ключевые слова: риск ликвидности, информационные технологии, архитектура, ETL-инструменты.

Fedorov B.M.

Postgraduate student, The Moscow state university of economics, statistics and informatics (MESI)

USE OF INFORMATION TECHNOLOGIES IN MANAGEMEN OF LIQUIDITY RISK IN A BANK

Abstract

The liquidity risk of a bank is one of the most important risks. The article describes the requirements for an information system to manage liquidity risk. The author describes the architectural features of the system including specific mechanisms.

Keywords: liquidity risk, information technologies, architecture, ETL-tool.

Introduction

In their daily activities for any organization the most important task of ensuring sustainable development and respect for their financial security is an effective risk management. Particularly relevant risk management in the banking sector. This is due to the high dependence on the banking sector indicators of financial markets, which are currently under pressure due to the instability of the financial markets, debt crisis in Europe and political unrest in the Middle East and Ukraine.

Risk division of a bank, depending on the changes of indicators to measure these or other risks, are obliged to act on their management, including the reduction of the probability of occurrence and risk minimization.[1] To be able to effectively manage risk, various information technology. This article discusses the steps for creating and using information system for managing liquidity risk in the bank.

Information technologies in management of liquidity risk in bank

One of the major banking risks is liquidity risk, which is a manifestation of the inability to rapid conversion of financial assets in the means of payment at affordable prices without lossless or attract additional obligations. [2] In management of liquidity risk, the problem of its evaluation is only one of the stages, even critical and fundamental in determining further action. For this purpose variety of methods. The most popular methods are: identification and control limits for various operations; stress testing of the banking activity. To implement these methods were used various information technologies. [3] Nevertheless, there is a need to combine different methods of managing this risk in a single information environment. The creation such a tool is necessary to determine the general algorithms work and develop its architecture. For this purpose it is proposed to use a certain conceptual positions that will be the basis to create the tool.

For solving the problem of association of risk management techniques and the implementation of a unified risk assessment models were formulated requirements for information system:

  • Relevance of data. For the information system to be used to date information from the Banking, which is necessary for the calculation. This reduce the likelihood of issuing incorrect results;
  • Modularity. Such system should consist of modules for risk assessment, stress testing, risk control limits. All modules should be independent, but must use the results of each other’s;
  • Integration. The data needed for the analysis and construction risk assessment model liquidity, must come from external sources and placed in the database
  • Information security. Additional requirements for information security. This is due to the fact that in the information system processes the data, which are confidential and bank secrecy.
  • Compatibility. Implementation of risk assessment models liquidity as software modules should ensure compatibility performance of the algorithm with existing liquidity management processes;

Based on the defined requirements for creating an information system to manage liquidity risk of the bank was developed architecture. The system architecture is build on the comprehensive integration of the following subsystems: evaluation of liquidity risk; stress-testing; limits management.

All subsystems use a single database, aggregating data from external sources. For the formation of the actual data in the database is developed and implemented an algorithm proposes as ETL-Tool. [4] ETL-tool provides data retrieval from other banking information systems, data transformation (bringing data to a common format, data integrity checking), subsequent loading, and using the ETL-tool adjusts the frequency of data loading. The part of the data enters an information system on-line, to form an accurate assessment of the bank’s liquidity risk.

Conclusion

The use of information system will improve the efficiency of bank liquidity management. Requirements and architecture of information systems have been used in the work of GPB (OJSC). As a result, the number of cases of violation of liquidity in October 2013 decreased by 22% compared with the same period in 2012.

References

  1. Altman E.I. Managing and Measuring Risk: Emerging Global Standards and Regulations After the Financial Crisis – World Scientific Publishing Company, 1-st edition, 2013. ISBN 978-9814417495.
  2. Stulz R.M. The Risks of Financial Institutions (National Bureau of Economic Research Conference Report). – University Of Chicago Press, 2007. – ISBN 978-0226092850.
  3. Fedorov B.М. Valuation methods of influence of e-payments on the bank liquidity risk level // “ECOMMIS” TEMPUS Project”, Berlin Institute of Technology, – Berlin, Germany – 2012.
  4. Passioned Group. ETL-tools and Data integration. – Passionned Nederland B.V., 2013.

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