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Operational Risk Assesement Tools for Quality Management in Banking Services

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  • Alina Mihaela Dima

    (The Bucharest Academy of Economic Studies, Romania)

Abstract

Among all the different types of risks that can affect financial companies, the operational risk can be the most devastating and the most difficult to anticipate. The management of operational risk is a key component of financial and risk management discipline that drives net income results, 2capital management and customer satisfaction. The present paper contains a statistical analysis in order to determine the number of operational errors as quality based services determinants, depending on the number of transactions performed at the branch unit level. Regression model applied to a sample of 418 branches of a major Romanian bank is used to guide the decision taken by the bank, consistent with its priorities of minimizing the risk and enlarging the customer base ensuring high quality services. The analyisis reveals that the model can predict the quality of the transactions based on the number of operational errors. Under Basel II, this could be a very helpful instrument for banks in order to adjust the capital requirement to the losses due to operational errors, predicted by the model.

Suggested Citation

  • Alina Mihaela Dima, 2009. "Operational Risk Assesement Tools for Quality Management in Banking Services," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 11(26), pages 364-372, June.
    • Handle: RePEc:aes:amfeco:v:11:y:2009:i:26:p:364-373
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Nicolae Dardac & Petronel Chiriac & Bogdan Moinescu, 2012. "The Use of Internal Rating Models in Managing the Risks Related to the Exposures of Non-banking Financial Institutions," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 14(31), pages 258-271, February.
    2. Curmei Cătălin-Valeriu & Dincă Lavinia Elena & Curmei-Semenescu Ileana Andreea, 2018. "The influence of the strategic financial policies on share valuation in an unstable economic environment," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 12(1), pages 241-250, May.
    3. Vergil Voineagu & Simona Nicoleta Vasilache & Daniela Şerban & Silvia Elena Cristache & Liviu Stelian Begu, 2016. "An Analysis of the Romanian E-Commerce Trade Trends in European Perspective," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(1), pages 235-252.
    4. Dervis Kirikkaleli & Pelin Yaylali & Okan Veli Safakli, 2020. "The Perception and Culture of Operational Risk in the Banking Sector: Evidence From Northern Cyprus," SAGE Open, , vol. 10(4), pages 21582440209, October.
    5. Lu Wei & Jianping Li & Xiaoqian Zhu, 2018. "Operational Loss Data Collection: A Literature Review," Annals of Data Science, Springer, vol. 5(3), pages 313-337, September.
    6. Vasilache Simona & Rînciog Johana, 2017. "Curricular improvements for entrepreneurial education," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 11(1), pages 302-311, July.
    7. Semenescu Andreea & Curmei Cătălin Valeriu, 2015. "Using CSR to mitigate information asymmetry in the banking sector," Management & Marketing, Sciendo, vol. 10(4), pages 316-329, December.

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    More about this item

    Keywords

    quality management; operational risk; banking services; binary regression model;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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