Risk Management最新文献_第4页

Non-performing loans and bank lending behaviour 不良贷款和银行贷款行为

Risk Management Pub Date : 2023-01-20 DOI: 10.1057/s41283-022-00111-z

Ardit Gjeçi, Matej Marinč, Vasja Rant

引用次数: 1

Assessing the importance of the choice threshold in quantifying market risk under the POT approach (EVT) 基于POT方法(EVT)评估选择阈值在量化市场风险中的重要性

Risk Management Pub Date : 2023-01-06 DOI: 10.1057/s41283-022-00106-w

Sonia Benito, Carmen López-Martín, M. Navarro

引用次数: 2

Risk measures-based cluster methods for finance 基于风险测度的金融聚类方法

Risk Management Pub Date : 2022-12-22 DOI: 10.1057/s41283-022-00110-0

P. C. Guedes, F. Müller, M. Righi

引用次数: 0

IRB Asset and Default Correlation: Rationale for the Macroprudential Mark-Ups to the IRB Risk-Weights 内部审查资产与违约相关性:宏观审慎对内部审查风险权重加价的基本原理

Risk Management Pub Date : 2022-12-16 DOI: 10.1057/s41283-022-00109-7

H. Penikas

引用次数: 0

Information security risk management terminology and key concepts 信息安全风险管理术语和关键概念

Risk Management Pub Date : 2022-12-16 DOI: 10.1057/s41283-022-00108-8

Michael Schmidt

引用次数: 1

Exploring the indirect links between enterprise risk management and the financial performance of SMEs 探讨企业风险管理与中小企业财务绩效之间的间接联系

Risk Management Pub Date : 2022-12-12 DOI: 10.1057/s41283-022-00107-9

Lenka Syrová, J. Špička

引用次数: 4

Addressing Data Gaps for Facility Reliability Assessments Using Non-Hierarchical Cluster Analysis 利用非层次聚类分析解决设施可靠性评估的数据缺口

Risk Management Pub Date : 2022-09-26 DOI: 10.1115/ipc2022-87145

Ryan Stewart, Martin Di Blasi, T. Dessein

{"title":"Addressing Data Gaps for Facility Reliability Assessments Using Non-Hierarchical Cluster Analysis","authors":"Ryan Stewart, Martin Di Blasi, T. Dessein","doi":"10.1115/ipc2022-87145","DOIUrl":"https://doi.org/10.1115/ipc2022-87145","url":null,"abstract":"\u0000 Performing reliability assessments for a large asset inventory of pipeline facility equipment, such as compressor station assets, requires a substantial dataset of attributes for a diverse range of equipment types. In many cases, equipment data inventories have gaps, with one or more required attributes unknown, such as diameter, wall thickness, operating pressure or material properties. The identification and collection of complete records is typically labor-intensive and time consuming, so data gaps are often filled with assumptions while ongoing data collection improves. A standard approach to fill these gaps is to use conservative assumptions for missing attributes. This results in missing data producing higher assessed risk than complete records. The benefit of this conservative approach is that it appropriately penalizes the incomplete records, driving action toward collecting the information where it matters. However, this approach is simple, does not leverage all the information available within the available dataset, and can produce a distorted representation of risk that may reduce the credibility of the risk assessment.\u0000 This paper describes a process to use unsupervised machine learning algorithms to organize large asset inventories into groups and fill data gaps with reasonable, but conservative assumptions. We used a non-hierarchical clustering method to group asset records into clusters. Instead of using the most conservative value to fill data gaps across all records, gaps are filled using the most conservative value from similar records. This method provides estimates for data gaps that are more realistic while still maintaining conservatism, striking a balance between prioritizing equipment with confirmed attributes that indicate higher risk and equipment with little information.\u0000 The approach described in this study relies on K-means clustering. We discuss the practical uses of dimensionality reduction, heuristic techniques for selecting the number of clusters, and sensitivity analysis.","PeriodicalId":21327,"journal":{"name":"Risk Management","volume":"1171 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86476598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Probabilistic Analysis Applied to the Risk of SCC Failure SCC失效风险的概率分析

Risk Management Pub Date : 2022-09-26 DOI: 10.1115/ipc2022-86906

Scott Riccardella, Owen M. Malinowski, P. Riccardella, S. Potts, Sean Moran, Kelly Thompson, Ann Reo

引用次数: 0

Effectiveness of Subsurface Drainage for Mitigation of Landslides Affecting Pipelines 地下排水对减少影响管道的滑坡的有效性

Risk Management Pub Date : 2022-09-26 DOI: 10.1115/ipc2022-87107

M. Rashidi, A. Ebrahimi, Arash Mosaiebian

{"title":"Effectiveness of Subsurface Drainage for Mitigation of Landslides Affecting Pipelines","authors":"M. Rashidi, A. Ebrahimi, Arash Mosaiebian","doi":"10.1115/ipc2022-87107","DOIUrl":"https://doi.org/10.1115/ipc2022-87107","url":null,"abstract":"\u0000 Landslides can pose a threat to the integrity of new and existing pipelines if they are not mitigated. Improving subsurface drainage of groundwater is one of the most widely used stabilization strategies for mitigating landslides affecting pipelines because subsurface drainage requires minimal design and costs and can improve the overall stability. The appropriate design and implementation of this approach could lower the groundwater table within the landslide as a primary factor triggering landslide movement by reducing the driving force and increasing the shear strength or resisting force within the landslide mass. Thus, the subsurface drains are conventionally employed in the mitigation of most landslides that may threaten pipelines either as a single strategy or in conjunction with other measures.\u0000 This paper presents how effective subsurface drainage systems are for improving slope stabilization in various site conditions. Included in the discussion is the predesign investigation considerations. The results of a series of two-dimensional limit equilibrium and seepage analyses are presented to evaluate the effectiveness of subsurface drainage systems. Site conditions explored in this paper include the location of the right-of-way compared to the boundary of landslide, geometry of landslide, and groundwater level. A model that uses genetic expression programming as a computational intelligence technique is introduced that predicts the effectiveness of subsurface drainage systems.","PeriodicalId":21327,"journal":{"name":"Risk Management","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88698974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distribution Pipeline Risk Framework 配电管道风险框架

Risk Management Pub Date : 2022-09-26 DOI: 10.1115/ipc2022-87066

Jason B. Skow, Ryan Stewart, Rob McPherson, Kent Schoenroth

{"title":"Distribution Pipeline Risk Framework","authors":"Jason B. Skow, Ryan Stewart, Rob McPherson, Kent Schoenroth","doi":"10.1115/ipc2022-87066","DOIUrl":"https://doi.org/10.1115/ipc2022-87066","url":null,"abstract":"\u0000 By nature, gas distribution is a network system; it does not fit well with traditional pipeline risk models that assume a linear geometry. Distribution system growth is multigenerational and often leads to mixed assets in the same area where transmission pipeline segments are often constructed within shorter time frames and more uniform materials. Slow, sporadic growth leads to varied record availability and quality that might not readily support commercially available risk models. The project described in this paper was initiated to develop predictive methods to prioritize mitigation and replacement activities for distribution networks. Priority is assigned to areas by risk, equipment characteristics and environmental attributes.\u0000 In a previous IPC paper, the authors developed a histori-calbased predictive model but applied it to a single city area. This work has been extended to cover the entire province of Saskatchewan. The model relies on logistic regression and a machine learning algorithm to associate the historical failure rate with the asset type, age, pipe material, diameter, pressure, and an array of geographical-dependent attributes such as soil properties and climate events. The output of the model allows integrity engineers to consider predicted failure rates to complement the lagging performance indicators used to develop integrity program planning. This model demonstrates the advantage of using available distribution system records to develop a custom historicalbased predictive model.\u0000 Consequence estimates for distribution networks are also described. Distribution leaks are often classified into hazard levels that differentiate operational response. These are assigned based on incident data records and SME input to develop an event tree for the consequence of a distribution leak. This paper summarizes the work performed during the project to calculate distribution asset probability of failure and consequences.","PeriodicalId":21327,"journal":{"name":"Risk Management","volume":"89 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90734798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0