MIRCE Science Based Operational Risk Assessment

2018 ◽  
pp. 223-255
Author(s):  
Jezdimir Knezevic
Keyword(s):  
Risk Assessment ◽  
Operational Risk

An alternative approach for the operational risk assessment of a new product

2019 ◽  
Author(s):  
Andrea Giacchero ◽  
Jacopo Moretti ◽  
Francesco Cesarone ◽  
Fabio Tardella
Keyword(s):  
Risk Assessment ◽  
Operational Risk ◽  
New Product ◽  
Alternative Approach

scholarly journals Methodological Framework for Operational Risk Assessment

2017 ◽  
Vol 26 (4) ◽  
pp. 19-34 ◽  
Author(s):  
Josef Procházka ◽  
Josef Melichar
Keyword(s):  
Risk Assessment ◽  
Operational Risk ◽  
Methodological Framework

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

UAV Swarm Operational Risk Assessment System

2015 ◽  
Author(s):  
Sariyu Marfo ◽  
Shane Ehler ◽  
Ryan Fields ◽  
Jamaries B. Negron ◽  
Shane Skopak ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Operational Risk ◽  
Assessment System ◽  
Uav Swarm

Integrated Barrier Analysis in Operational Risk Assessment in Offshore Petroleum Operations

2004 ◽  
pp. 620-625 ◽  
Author(s):  
Jan Erik Vinnem ◽  
Terje Aven ◽  
Stein Hauge ◽  
Jorunn Seljelid ◽  
Gunnar Veire
Keyword(s):  
Risk Assessment ◽  
Operational Risk ◽  
Barrier Analysis ◽  
Offshore Petroleum

scholarly journals A Behavioral Probabilistic Risk Assessment Framework for Managing Autonomous Underwater Vehicle Deployments

2012 ◽  
Vol 29 (11) ◽  
pp. 1689-1703 ◽  
Author(s):  
Mario Brito ◽  
Gwyn Griffiths ◽  
James Ferguson ◽  
David Hopkin ◽  
Richard Mills ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Autonomous Underwater Vehicle ◽  
Risk Mitigation ◽  
Operational Risk ◽  
Maximum Error ◽  
Underwater Vehicle ◽  
Behavioral Approach ◽  
Mitigation Measures ◽  
The Arctic ◽  
Deep Diving

Abstract The deployment of a deep-diving long-range autonomous underwater vehicle (AUV) is a complex operation that requires the use of a risk-informed decision-making process. Operational risk assessment is heavily dependent on expert subjective judgment. Expert judgments can be elicited either mathematically or behaviorally. During mathematical elicitation experts are kept separate and provide their assessment individually. These are then mathematically combined to create a judgment that represents the group view. The limitation with this approach is that experts do not have the opportunity to discuss different views and thus remove bias from their assessment. In this paper, a Bayesian behavioral approach to estimate and manage AUV operational risk is proposed. At an initial workshop, behavioral aggregation, that is, reaching agreement on the distributions of risks for faults or incidents, is followed by an agreed upon initial estimate of the likelihood of success of the proposed risk mitigation methods. Postexpedition, a second workshop assesses the new data and compares observed to predicted risk, thus updating the prior estimate using Bayes’ rule. This feedback further educates the experts and assesses the actual effectiveness of the mitigation measures. Applying this approach to an AUV campaign in ice-covered waters in the Arctic showed that the maximum error between the predicted and the actual risk was 9% and that the experts’ assessments of the effectiveness of risk mitigation led to a maximum of 24% in risk reduction.

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