scholarly journals New Tools to Support the Risk Assessment Process of Nanomaterials in the Insurance Sector

2021 ◽  
Vol 18 (13) ◽  
pp. 6985
Author(s):  
Francisco Aznar Mollá ◽  
Carlos Fito-López ◽  
Jose Antonio Heredia Alvaro ◽  
Francisco Huertas-López
Keyword(s):  
Assessment Process ◽  
Proper Understanding ◽  
Risk Assessment Process ◽  
Main Route ◽  
Analysis And Evaluation ◽  
Insurance Sector ◽  
Insurance Contracts ◽  
Emerging Risk ◽  
Wear And Tear ◽  
Route Of Exposure

During the last decade, the use of nanomaterials, due to their multiple utilities, has exponentially increased. Nanomaterials have unique properties such as a larger specific surface area and surface activity, which may result in health and environmental hazards different from those demonstrated by the same materials in bulk form. Besides, due to their small size, they can easily penetrate through the environmental and biological barriers. In terms of exposure potential, the vast majority of studies are focused on workplace areas, where inhalation is the most common route of exposure. The main route of entry into the environment is due to indirect emissions of nanomaterials from industrial settings, as well as uncontrollable releases into the environment during the use, recycling and disposal of nano-enabled products. Accidental spills during production or later transport of nanomaterials and release from wear and tear of materials containing nanomaterials may lead to potential exposure. In this sense, a proper understanding of all significant risks due to the exposure to nanomaterials that might result in a liability claim has been proved to be necessary. In this paper, the utility of an application for smartphones developed for the insurance sector has been validated as a solution for the analysis and evaluation of the emerging risk of the application of nanotechnology in the market. Different exposure scenarios for nanomaterials have been simulated with this application. The results obtained have been compared with real scenarios, corroborating that the use of novel tools can be used by companies that offer risk management in the form of insurance contracts.

A methodology for e-banking risk assessment using fuzzy logic and Bayesian network

2021 ◽  
Vol 19 (3) ◽  
pp. 101-124
Author(s):  
Ako Rita Erhovwo ◽  
Okpako Abugor Ejaita ◽  
Duke Oghorodi
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Bayesian Network ◽  
Operational Risk ◽  
Assessment Process ◽  
Exposure Level ◽  
Model Assessment ◽  
Assessment Methodology ◽  
Risk Assessment Process ◽  
Risk Assessment Methodology

Risk assessment methodology in general has been around for quite a while, its prominence in the E-banking field is a fairly recent phenomenon. We are at the point where risk assessments are critical to the overall function of banks. Banks are required to assess the processes underlying their operations against potential threats, vulnerabilities, and their potential impact, which helps in revealing the risk exposure level, and the residual risks. Identifying clearly a risk assessment methodology is often the first step of assessing and evaluating risk associated with an organization operation. This paper presents a risk assessment methodology for Ebanking Operational Risk. The proposed risk assessment methodology consists of four major steps: a risk model, assessment approach, analysis approach and a risk assessment process. The main tool of the proposed risk assessment methodology is the risk assessment process. The assessment process gives detailed explanation with respect to which models or techniques may be applied and how they are expressed. In this paper the risk assessment technique is built upon fuzzy logic (FL) concept and Bayesian network (BN). In fuzzy logic, an element is included with a degree of membership. Bayesian network is an inference classifier that is capable of representing conditional independencies. The Bayesian and fuzzy logic–based risk assessment process gives good predictions for risk learning and inference in the E-banking systems. Keywords: Fuzzy logic, Bayesian network, risk assessment methodology, operational risk, Ebanking

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