An integrated inverse adaptive neural fuzzy system with Monte-Carlo sampling method for operational risk management

Peña, Alejandro; Bonet, Isis; Lochmuller, Christian; Chiclana, Francisco; Góngora, Mario

doi:10.1016/j.eswa.2018.01.001

Cited by 24 publications

(17 citation statements)

References 31 publications

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“…The U-ET on the right side of the bowtie model complements the fault tree and reveals the critical event as the initiating event. The major elements of an event tree (events) are usually identified as accident escalation factors or safety barriers [37]. In this paper, safety barriers are taken as an example in which an uncertain random variable will be defined to describe the state of an event leading to failure or success and U-ETA based on chance theory is used to calculate the ''risk belief'' of the outcome events.…”

Section: U-boetie Model Construction a Model Constructionmentioning

confidence: 99%

Aviation Risk Analysis: U-bowtie Model Based on Chance Theory

Wang

Ming

et al. 2019

IEEE Access

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Risk analysis is a crucial component in any aviation application. The conventional risk analysis schemes for systems encompassing large amounts of data are based on the probability theory. However, in some practical systems, the components of complex systems may have few or even zero samples, in which case the risk cannot be evaluated simply via statistics. This paper proposes a novel aviation risk analysis scheme based on the chance theory, which is a generation of both probability theory and uncertainty theory. In this paper, a U-bowtie model is first established according to the graphic structure and step-wise analysis process. A risk belief metric is utilized, and the risk variable is modified by a logic algorithm based on the operational law of chance theory. Then, several risk belief formulas are built for different respective system configurations; the risk of output events in the U-bowtie model can be calculated via the corresponding formulas. Additionally, the most contributed basic event and the most effective measurement can be obtained through sensitivity analysis. The effectiveness of the proposed scheme is validated by a case study. The results presented here may represent an innovative approach to managing risk in larger classes of complex systems.INDEX TERMS Aviation risk, chance theory, U-bowtie analysis, sensitivity analysis.

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Section: U-boetie Model Construction a Model Constructionmentioning

confidence: 99%

Aviation Risk Analysis: U-bowtie Model Based on Chance Theory

Wang

Ming

et al. 2019

IEEE Access

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“…The characterization of multi-rates financial scenarios based on fuzzy concepts is based on the characterization of the different rates as linguistic random variables, for which the following definitions are proposed [49]: Definition 1. Random variables are variables that can take different values with different probabilities based on a probability distribution.…”

Section: Fuzzy Characterization Of Financing Ratesmentioning

confidence: 99%

“…In accordance with the fuzzy sets obtained from a cdf , for the development of scenarios a new fuzzy sampling, which is based on the Monte Carlo method is proposed. This method has the following structure [49]:…”

Section: Fuzzy Characterization Of Financing Ratesmentioning

confidence: 99%

“…To achieve a reliability closed to 99.9%, the sampling process was carried out for a total of 1000 records per linguistic input variable, where the sampling interval for each variable was defined by the distance between the values of the variables defined for each scenario, as shown in Table 10. The statistical properties for each variable are defined by the structure and shape of the fuzzy sets that characterize a linguistic rate described in [49]. Table 11 displays a set of distributions that allow modelling the EIRR as a probability distribution, as a result of the Monte Carlo sampling process carried out on the S F LM M structure.…”

Section: Grltmentioning

confidence: 99%

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Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

González-Ruiz

Peña

Grisales

et al. 2019

Applied Soft Computing

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In general, the development of economic infrastructure systems requires a behavioural comprehensive analysis of different financial variables or rates to establish its long-term success with regards to the Equity Internal Rate of Return (EIRR) expectation. For this reason, several financial organizations have developed economic scenarios supported by computational techniques and models to identify the evolution of these financial rates. However, these models and techniques have shown a series of limitations with regard to the financial management process and its impact on EIRR over time. To address these limitations in an inclusive way, researchers have developed different approaches and methodologies focused on the development of financial models using stochastic simulation methods and computational intelligence techniques. This paper proposes a Stochastic Fuzzy Logistic Model (S-FLM) inspired by a Fuzzy Cognitive Map (FCM) structure to model financial scenarios. Where the input consists in financial rates that are characterized as linguistic rates through a series of adaptive logistic functions. The stochastic process that explains the behaviour of the financial rates over time and their partial effects on EIRR is based on a Monte Carlo sampling process carried out on the fuzzy sets that characterize each linguistic rate. The S-FLM was evaluated by applying three financing scenarios to an airport infrastructure system (pessimistic, moderate/base, optimistic), where it was possible to show the impact of different linguistic rates on the EIRR. The behaviour of the S-FLM was validated using three different models: (1) a financial management tool; (2) a general FCM without pre-loaded causalities among the variables; and (3) a Statistical S-FLM model (S-FLMS), where the causalities between the concepts or rates were obtained as a result of an independent effects analysis applying a cross modelling between variables and by using a statistical multi-linear model (statistical significance level) and a multi-linear neural model (MADALINE). The results achieved by the S-FLM show a higher EIRR than expected for each scenario. This was possible due to the incorporation of an adaptive multi-linear causality matrix and a fuzzy credibility matrix into its structure. This allowed to stabilize the effects of the financial variables or rates on the EIRR throughout a financing period. Thus, the S-FLM can be considered as a tool to model dynamic financial scenarios in different knowledge areas in a comprehensive manner. This way, overcoming the limitations imposed by the traditional computational models used to design these financial scenarios.

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“…This is because its source -apart from the environment and external circumstances (including the economic cycle) -lies in the bank organization itself [Wagner, Mizgier, Papageorgiou 2017]. The concept of capital requirements for operational risk calculation proposed in 2004 was revolutionary also in terms of its structure, which introduced a variety of calculation methods, including the advanced models developed by the banks [Peña et al 2018]. It was this variety of methods and huge discrepancy between the simplicity of some methods and complexity of others which turned into the greatest disadvantage of the whole concept and encouraged the Basel Committee -after the global financial crisis which broke out in 2008 -to seek a new approach to the capital requirements for operational risk [Feria-Domínguez, Jiménez-Rodríguez, Sholarin 2015].…”

Section: Introductionmentioning

confidence: 99%

Operational Risk in Banks – Revolution or Regulatory Evolution

Koleśnik¹

2018

PRACE NAUKOWE UNIWERSYTETU EKONOMICZNEGO WE WROCŁAWIU

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The aim of the article is to compare the methods of calculating capital requirement for operational risk in the banks with the new approach announced by the Basel Committee in December 2017. The analysis also demonstrated that the new rules are a genuine revolution in the field of comparability of capital requirements between all banks and evolution in the methodology of its calculation. Introduction of a single method of calculation of capital requirements for all banks instead of four methods and five variants of their combination applied so far will provide real comparability of capital requirements between banks for all stockholders. Considerable discretion of bank supervision, which allowed for unfounded interference with the method used by the bank, will also be eliminated. What is important, the new method-as compared to most of the methods used so far-will take into account data reflecting real bank exposure to operational losses.

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An integrated inverse adaptive neural fuzzy system with Monte-Carlo sampling method for operational risk management

Cited by 24 publications

References 31 publications

Aviation Risk Analysis: U-bowtie Model Based on Chance Theory

Aviation Risk Analysis: U-bowtie Model Based on Chance Theory

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

Operational Risk in Banks – Revolution or Regulatory Evolution

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