IDEAS home Printed from https://ideas.repec.org/a/scn/financ/y2018i4p130-145.html
   My bibliography  Save this article

Ключевые Показатели Эффективности Работы С Проблемными Активами Банка И Их Расчет // Key Performance Indicators Of The Bank’S Distressed Assets And Their Calculation

Author

Listed:
  • R. Dolzhenko A.

    (The ural State university of Economics)

  • Р. Долженко А.

    (Уральский государственный экономический университет)

Abstract

At the present, the success of a credit institution is possible only if it can organize effective work with distressed assets. They can be managed through a system of key performance indicators, which allows decomposition of the goals of the organizational unit to specific, measurable indicators. The purpose of this article is a presentation of key performance indicators (KPI), grounded on the formation of the basic principles of management of distressed assets, the methodological approaches to their assessment and calculations, and also formulas for the calculation of specific KPIs. My research was conducted using the methods of theoretical knowledge, logical methods and methods of comparative analysis. First, I have identified the main signs of the problem of the consumers’ indebtedness, as well as a list of documents on the basis of which it is possible to conclude that the assets are distressed. Secondly, I have outlined the main tools for working with the bank’s distressed assets, implemented in the framework of the bank’s strategies of action. Thirdly, I identified key performance indicators of operations with distressed assets of the bank, methodological approaches to their evaluation, the sources for the calculation of the key performance indicators of operations with distressed assets of the bank, as well as the basic formulas by which one can calculate the necessary components of the KPI. The use of the proposed KPIs will allow the Bank to solve strategic and tactical (operational) tasks in the field of working with distressed assets, as well as to achieve the growth of the quality of work with distressed debts. The KPIs proposed in this article can be introduced into the system of assessing the activities of the division as a whole and its managers for working with distressed debts and can become the basis for the system of staff incentives. В современных условиях успех кредитной организации возможен в том случае, если она сможет организовать эффективную работу с проблемными активами. Управлять ими можно через систему ключевых показателей эффективности, позволяющую декомпозировать цели деятельности подразделения до конкретных измеримых показателей. Цель работы — на основе формирования базовых принципов управления проблемными активами выделить ключевые показатели эффективности (КПЭ), определить методические подходы к их оценке и расчетам, предложить формулы для расчета конкретных КПЭ.Исследование проводилось с использованием методов теоретического познания, логических методов и методов сравнительного анализа.Выделены основные признаки проблемности задолженности физических лиц, а также представлен перечень документов, на основании которых можно сделать вывод о проблемности актива. Обозначены основные инструменты работы с проблемными активами банка, реализуемые в предусмотренных для этого стратегиях действий банка. Выделены ключевые показатели эффективности работы с проблемными активами банка, методические подходы к их оценке, источники для расчета основных ключевых показателей эффективности работы с проблемными активами банка, а также базовые формулы, по которым можно рассчитать необходимые компоненты КПЭ.Использование предлагаемых КПЭ позволит банку решить стратегические и тактические (операционные) задачи в области работы с проблемными активами, а также добиться роста качества работы с проблемной задолженностью. Выделенные в работе КПЭ могут быть внедрены в систему оценки деятельности подразделения по работе с проблемной задолженностью и его менеджеров, стать основой для системы стимулирования персонала.

Suggested Citation

  • R. Dolzhenko A. & Р. Долженко А., 2018. "Ключевые Показатели Эффективности Работы С Проблемными Активами Банка И Их Расчет // Key Performance Indicators Of The Bank’S Distressed Assets And Their Calculation," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 22(4), pages 130-145.
  • Handle: RePEc:scn:financ:y:2018:i:4:p:130-145
    as

    Download full text from publisher

    File URL: https://financetp.fa.ru/jour/article/viewFile/739/508.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wozabal, David & Hochreiter, Ronald, 2012. "A coupled Markov chain approach to credit risk modeling," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 403-415.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    2. Tamás Kristóf, 2021. "Sovereign Default Forecasting in the Era of the COVID-19 Crisis," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    3. D. V. Boreiko & Y. M. Kaniovski & G. Ch. Pflug, 2017. "Numerical Modeling of Dependent Credit Rating Transitions with Asynchronously Moving Industries," Computational Economics, Springer;Society for Computational Economics, vol. 49(3), pages 499-516, March.
    4. D. V. Boreiko & Y. M. Kaniovski & G. Ch. Pflug, 2016. "Modeling dependent credit rating transitions: a comparison of coupling schemes and empirical evidence," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 24(4), pages 989-1007, December.
    5. David Conaly Martínez Vázquez & Christian Bucio Pacheco & Alejandra Cabello Rosales, 2021. "Proyección Markoviana para 2020 y 2021 de las Calificaciones Corporativas en México," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(1), pages 1-21, Enero - M.
    6. David Conaly Martínez Vázquez & Christian Bucio Pacheco & Alejandra Cabello Rosales, 2021. "Proyección Markoviana para 2020 y 2021 de las Calificaciones Corporativas en México," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(1), pages 1-21, Enero - M.
    7. W. Hölzl & S. Kaniovski & Y. Kaniovski, 2019. "Exploring the dynamics of business survey data using Markov models," Computational Management Science, Springer, vol. 16(4), pages 621-649, October.
    8. T. Gärtner & S. Kaniovski & Y. Kaniovski, 2021. "Numerical estimates of risk factors contingent on credit ratings," Computational Management Science, Springer, vol. 18(4), pages 563-589, October.
    9. Dmitri Boreiko & Serguei Kaniovski & Yuri Kaniovski & Georg Ch. Pflug, 2018. "Business Cycles and Conditional Credit-Rating Migration Matrices," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 1-19, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:scn:financ:y:2018:i:4:p:130-145. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Алексей Скалабан (email available below). General contact details of provider: http://financetp.fa.ru .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.