An Integrated Inverse Adaptive Neural Fuzzy System with Monte-Carlo Sampling Method for Operational Risk Management

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dc.contributor.author Chiclana, Francisco en
dc.contributor.author Gongora, Mario Augusto en
dc.contributor.author Pena, Alejandro en
dc.contributor.author Bonet, Isis en
dc.contributor.author Lochmuller, Christian en
dc.date.accessioned 2018-01-02T16:00:29Z
dc.date.available 2018-01-02T16:00:29Z
dc.date.issued 2018-01
dc.identifier.citation Pena, A. et al. (2018) An Integrated Inverse Adaptive Neural Fuzzy System with Monte-Carlo Sampling Method for Operational Risk Management. Expert Systems with Application. en
dc.identifier.issn 0957-4174
dc.identifier.uri http://hdl.handle.net/2086/15046
dc.description The file attached to this record is the author's final peer reviewed version. en
dc.description.abstract Operational risk refers to deficiencies in processes, systems, people or external events, which may generate losses for an organization. The Basel Committee on Banking Supervision has defined different possibilities for the measurement of operational risk, although financial institutions are allowed to develop their own models to quantify operational risk. The advanced measurement approach, which is a risk-sensitive method for measuring operational risk, is the financial institutions preferred approach, among the available ones, in the expectation of having to hold less regulatory capital for covering operational risk with this approach than with alternative approaches. The advanced measurement approach includes the loss distribution approach as one way to assess operational risk. The loss distribution approach models loss distributions for business-line-risk combinations, with the regulatory capital being calculated as the 99,9% operational value at risk, a percentile of the distribution for the next year annual loss. One of the most important issues when estimating operational value at risk is related to the structure (type of distribution) and shape (long tail) of the loss distribution. The estimation of the loss distribution, in many cases, does not allow to integrate risk management and the evolution of risk; consequently, the assessment of the effects of risk impact management on loss distribution can take a long time. For this reason, this paper proposes a flexible integrated inverse adaptive fuzzy inference model, which is characterized by a Monte-Carlo behavior, that integrates the estimation of loss distribution and different risk profiles. This new model allows to see how the management of risk of an organization can evolve over time and it effects on the loss distribution used to estimate the operational value at risk. The experimental study results, reported in this paper, show the flexibility of the model in identifying (1) the structure and shape of the fuzzy input sets that represent the frequency and severity of risk; and (2) the risk profile of an organization. Therefore, the proposed model allows organizations or financial entities to assess the evolution of their risk impact management and its effect on loss distribution and operational value at risk in real time. en
dc.language.iso en_US en
dc.publisher Elsevier en
dc.subject Monte-Carlo sampling en
dc.subject Integrated adaptive neural fuzzy system en
dc.subject Loss Distribution Approach en
dc.subject Operational Value at Risk en
dc.subject Risk profile en
dc.subject Basel Committee on Banking Supervision en
dc.title An Integrated Inverse Adaptive Neural Fuzzy System with Monte-Carlo Sampling Method for Operational Risk Management en
dc.type Article en
dc.identifier.doi https://doi.org/10.1016/j.eswa.2018.01.001
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en
dc.explorer.multimedia No en
dc.funder N/A en
dc.projectid N/A en
dc.cclicence CC-BY-NC-ND en
dc.date.acceptance 2018-01-01 en


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