A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events
Operational Risk (OpR) refers to the possibility of suffering losses resulting from inadequate or failure of processes and/or technology, inadequate behaviour of people or external events. OpR was one of the main risks that led to the 2008 global financial crisis. Limitations of the analytical models that are applied in estimating this risk surface when qualitative information, frequently associated with OpR events, is used. To determine the magnitude of OpR in financial organisations, qualitative datainnd also historical data from risk events can be used. Current research trends that focus on the development of analytical models, by using different databases, to estimate the Operational Value at Risk (OpVaR) still lack models based on qualitative information, risk management profiles and the ability to integrate different databases of OpR events. In this paper we present a fuzzy model to estimate the OpVaR of an organisation by working with two different databases that contain internal available data and external or observed data. The proposed model considers: (1) the intrinsic properties of the data as fuzzy sets related to the linguistic variables of the observed data (external) and the data from available databases (internal), and (2) a series of management profiles to mitigate the effect that external data usually causes in estimating the OpVaR of an organisation. The results obtained with the proposed model allow an organisation to estimate and determine the behaviour of the OpVaR over time by using different risk profiles. The integration of qualitative information, different risk profiles (ranging from weak to strong risk management), and internal and external databases contributes to the advancement of estimating the OpVaR in risk management.
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Citation : Pena, A., Bonet, I., Lochmuller, C., Patiño, H.A., Chiclana, F., Gógora. M. (2018) A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events. Knowledge-Based Systems, 159, pp. 98-109
ISSN : 0950-7051
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes