Risk-based screening: Streamlining the baseline risk assessment process at federal facilities

1996 ◽  
Vol 7 (1) ◽  
pp. 15-26
Author(s):  
Ann Baines ◽  
John Haasbeek ◽  
Mary Jane Stell
Keyword(s):  
Risk Assessment ◽  
Assessment Process ◽  
Baseline Risk ◽  
Risk Assessment Process

Cumulative Risk Assessment Conceptual Model

2010 ◽  
Author(s):  
Lorna Harron ◽  
Doug McCutcheon
Keyword(s):  
Risk Assessment ◽  
Natural Gas ◽  
Cumulative Risk ◽  
The United States ◽  
Assessment Process ◽  
Annual Reports ◽  
Baseline Risk ◽  
Risk Level ◽  
Risk Assessment Process ◽  
Operating Risk

The energy transportation network of the United States consists of over 2.5 million miles of pipelines operated by approximately 3,000 companies. Based on data generated from annual reports to PHMSA from pipeline operators, the network includes approximately: • 173,000 miles of hazardous liquid pipeline; • 324,000 miles of gas transmission and gathering pipelines; • 2,037,000 miles of natural gas distribution mains and service pipelines; • 113 LNG plants connected to natural gas systems. There are 580,000 kilometers of pipeline in Canada, transferring oil and natural gas to various locations within the country, North America, and to ports, where products can then be shipped globally. As organizations change and grow, there is a need to determine not only the risk of a specific project or new asset, but the effect of that project or new asset on the risk profile of the facility or pipeline. Different types of risk evaluations may be performed at a location, so obtaining a risk score that can integrate various risk assessment techniques can be a challenge. This paper proposes a new technique developed to meet this need, called the cumulative risk assessment process. The cumulative risk assessment provides a quantified value for the operating risk at a facility based on the following formulae: BaselineRiskValue=L×C(1)OperatingRiskValue=BaselineRiskValue×(1×10−MitCredits)(2)CumulativeRiskValue=Σ(OperatingRiskValue)scen(3) Baseline risk is defined as the risk value in the absence of mitigation or risk control. Operating risk is the current risk level with existing mitigation and risk controls in place, evaluated in the calculation as mitigation credits. For the baseline risk calculation (L) refers to Likelihood and (C) refers to Consequence. Both baseline and operating risk are evaluated per scenario, with all scenarios summed to obtain the cumulative risk value for a location, pipeline or pipeline segment. This paper describes the cumulative risk assessment process and provides examples of how this risk assessment technique can be applied to an existing facility with new assets constructed and to a segment of operating mainline pipe.

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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