Applying fuzzy scenarios for the measurement of operational risk

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

doi:10.1016/j.asoc.2021.107785

Cited by 5 publications

(6 citation statements)

References 28 publications

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“…A fourth development trend focuses on the design of parametric insurances based on the characterisation of operational risk (OR) for different management scenarios [41]. Within this trend, the first group of studies shows how the ML concepts have been used to model OR [42].…”

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confidence: 99%

Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach

et al. 2022

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Given the challenges in reducing greenhouse gases (GHG), one of the sectors that have attracted the most attention in the Sustainable Development Agenda 2030 (SDA-2030) is the agricultural sector. In this context, one of the crops that has had the most remarkable development worldwide has been oil-palm cultivation, thanks to its high productive potential and being one of the most efficient sources of palmitic acid production. However, despite the significant presence of oil palm in the food sector, oil-palm crops have not been exempt from criticism, as its cultivation has developed mainly in areas of ecological conservation around the world. This criticism has been extended to other crops in the context of the Sustainable Development Goals (SDG) due to insecticides and fertilisers required to treat phytosanitary events in the field. To reduce this problem, researchers have used unmanned aerial vehicles (UAVs) to capture multi-spectral aerial images (MAIs) to assess fields’ plant vigour and detect phytosanitary events early using vegetation indices (VIs). However, detecting phytosanitary events in the early stages still suggests a technological challenge. Thus, to improve the environmental and financial sustainability of oil-palm crops, this paper proposes a hybrid deep-learning model (stacked–convolutional) for risk characterisation derived from a phytosanitary event, as suggested by lethal wilt (LW). For this purpose, the proposed model integrates a Lagrangian dispersion model of the backward-Gaussian-puff-tracking type into its convolutional structure, which allows describing the evolution of LW in the field for stages before a temporal reference scenario. The results show that the proposed model allowed the characterisation of the risk derived from a phytosanitary event, (PE) such as lethal wilt (LW), in the field, promoting improvement in agricultural environmental and financial sustainability activities through the integration of financial-risk concepts. This improved risk management will lead to lower projected losses due to a natural reduction in insecticides and fertilisers, allowing a balance between development and sustainability for this type of crop from the RSPO standards.

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confidence: 99%

Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach

et al. 2022

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“…Additional challenges encompass abundant qualitative information, uncertainty, and the low frequency of events (Bonet Peña et al, 2021), along with the potential existence of data experienced by other companies or extraordinary events that may not be captured in the company's historical data. These challenges prompt scholars to propose innovative solutions, such as combining LDA with scenario methods, to enhance the risk event data.…”

Section: Discussionmentioning

confidence: 99%

“…In operational risk modeling, scenario analysis is a way to assume the magnitude of losses that will be incurred and the frequency of operational risk events that a financial institution may face. Scenarios enrich operational risk event data by simulating events that have not yet occurred but may occur in the future or have already occurred in other organizations (Bonet B. et al, 2021).…”

Section: Scenario Analysismentioning

confidence: 99%

“…Sharma (2020) defines scenario analysis as a systematic process of obtaining expert opinions from business managers and risk management experts to assess the likelihood and impact of reasonable operational losses. One of the perceived benefits of scenario analysis is that it generates data that can be used to supplement historical data, especially at the distribution end (Pena et al, 2021;Bonet B. et al, 2021;Sharma, 2020). For example, it is possible to build optimistic, pessimistic, and disaster scenarios (catastrophes) for operational losses (Sharma, 2020).…”

Section: Scenario Analysismentioning

confidence: 99%

“…Therefore, some experts propose approaches that integrate techniques tailored to specific scenarios. Scenarios enrich data on operational risk events by performing simulations that have not yet occurred and are, therefore, not part of an organization's internal database but may occur in the future or have already occurred in other companies (Bonet B et al, 2021). propose scenarios with fuzzy methods to add risk scenarios as a valuable data source to the data used for operational risk measurement.…”

Section: Introductionmentioning

confidence: 99%

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Is the hybrid method more adequate for measuring operational risk?

Farsiah,

Amalia,

Saharuddin

et al. 2024

JAI

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Research aims: Risk management in financial institutions struggles with setting suitable capital charges for operational losses, resulting in large, disproportionate reserves that impact profits. This study, therefore, aims to develop a tailored operational risk measurement model for general takaful companies, addressing this challenge and optimizing capital allocation.Design/Methodology/Approach: This study employed a hybrid approach, merging the loss distribution approach (LDA) with historical data and scenario analysis for insurance company loss events. Compiling data into distributions, it utilized Monte Carlo simulations to determine value at risk (VaR). The resulting VaR guided the calculation of operational risk capital charges for future periods.Research findings: Measurement using the hybrid method could produce more adequate operational risk capital charges. These results confirm the acceptability of the VaR calculation and have been validated by the Kupic test.Theoretical contribution/Originality: This research offers a more comprehensive alternative method of measuring operational risk by combining historical company data with expert opinions, making it more likely to be practiced in the industry.Practitioner/Policy implication: The results of this study put forward an alternative, more suitable model for industry and regulators to measure operational risk management in general takaful companies.

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