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Operational Risk Management using a Fuzzy Logic Inference System

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  • Alejandro Reveiz
  • Carlos León

Abstract

Operational Risk (OR) results from endogenous and exogenous risk factors, as diverse and complex to assess as human resources and technology, which may not be properly measured using traditional quantitative approaches. Engineering has faced the same challenges when designing practical solutions to complex multifactor and non-linear systems where human reasoning, expert knowledge or imprecise information are valuable inputs. One of the solutions provided by engineering is a Fuzzy Logic Inference System (FLIS). Despite the goal of the FLIS model for OR is its assessment, it is not an end in itself. The choice of a FLIS results in a convenient and sound use of qualitative and quantitative inputs, capable of effectively articulating risk management's identification, assessment, monitoring and mitigation stages. Different from traditional approaches, the proposed model allows evaluating mitigation efforts ex-ante, thus avoiding concealed OR sources from system complexity build-up and optimizing risk management resources. Furthermore, because the model contrasts effective with expected OR data, it is able to constantly validate its outcome, recognize environment shifts and issue warning signals.

Suggested Citation

  • Alejandro Reveiz & Carlos León, 2009. "Operational Risk Management using a Fuzzy Logic Inference System," Borradores de Economia 574, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:574
    DOI: 10.32468/be.574
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    References listed on IDEAS

    as
    1. Sergio Scandizzo, 2005. "Risk Mapping and Key Risk Indicators in Operational Risk Management," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 34(2), pages 231-256, July.
    2. Isabelle Huault & V. Perret & S. Charreire-Petit, 2007. "Management," Post-Print halshs-00337676, HAL.
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    Cited by:

    1. Carlos León & Clara Machado & Andrés Murcia, 2013. "Macro-prudential assessment of Colombian financial institutions’ systemic importance," Borradores de Economia 800, Banco de la Republica de Colombia.
    2. Carlos León & Clara Machado & Andrés Murcia, 2016. "Assessing Systemic Importance With a Fuzzy Logic Inference System," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(1-2), pages 121-153, January.
    3. Youssef Lamrani Alaoui & Mohamed Tkiouat, 2017. "Managing Operational Risk Related to Microfinance Lending Process using Fuzzy Inference System based on the FMEA Method: Moroccan Case Study," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 64(4), pages 459-471, December.

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    More about this item

    Keywords

    Operational Risk; Fuzzy Logic; Risk Management;
    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
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

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