The Experts below are selected from a list of 49989 Experts worldwide ranked by ideXlab platform
Svetlana Suloeva - One of the best experts on this subject based on the ideXlab platform.
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development of project Risk Rating for telecommunication company
NEW2AN, 2016Co-Authors: Sergei Grishunin, Svetlana SuloevaAbstract:We developed the project Risk Rating (PRR) for telecommunication companies. It provides qualitative Risk scores assessment of capital expenditures (capex) projects to rank them by severity of exposures, to check their fit into the company’s Risk profile and, ultimately, to combine projects into the efficient project portfolio with the lowest Risk given return. We discussed the definition, functions and advantages of investment controlling and presented the reference model of its main subsystem – project portfolio controlling responsible for building the efficient capex project portfolio. Then, we developed the model of PRR; worked out the example of PRR’s scorecard and discussed the advantages of the PRR over the existing Risk assessment tools in project portfolio management.
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NEW2AN - Development of Project Risk Rating for Telecommunication Company
Lecture Notes in Computer Science, 2016Co-Authors: Sergei Grishunin, Svetlana SuloevaAbstract:We developed the project Risk Rating (PRR) for telecommunication companies. It provides qualitative Risk scores assessment of capital expenditures (capex) projects to rank them by severity of exposures, to check their fit into the company’s Risk profile and, ultimately, to combine projects into the efficient project portfolio with the lowest Risk given return. We discussed the definition, functions and advantages of investment controlling and presented the reference model of its main subsystem – project portfolio controlling responsible for building the efficient capex project portfolio. Then, we developed the model of PRR; worked out the example of PRR’s scorecard and discussed the advantages of the PRR over the existing Risk assessment tools in project portfolio management.
Andrew Mapstone - One of the best experts on this subject based on the ideXlab platform.
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Subsidence Risk Rating report - Wessex Searches
2017Co-Authors: Andrew MapstoneAbstract:SUBSIDENCE Risk Rating REPORT One in five homes - 6.5 million properties - are at Risk of beingaffected by shrink/swell subsidence , according to the British GeologicalSurvey. Before a property is purchased, subsidence
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Subsidence Risk Rating report
2016Co-Authors: Andrew MapstoneAbstract:SUBSIDENCE Risk Rating REPORT One in five homes - 6.5 million properties - are at Risk of beingaffected by shrink/swell subsidence , according to the British GeologicalSurvey. Before a property is purchased, subsidence
Sergei Grishunin - One of the best experts on this subject based on the ideXlab platform.
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development of project Risk Rating for telecommunication company
NEW2AN, 2016Co-Authors: Sergei Grishunin, Svetlana SuloevaAbstract:We developed the project Risk Rating (PRR) for telecommunication companies. It provides qualitative Risk scores assessment of capital expenditures (capex) projects to rank them by severity of exposures, to check their fit into the company’s Risk profile and, ultimately, to combine projects into the efficient project portfolio with the lowest Risk given return. We discussed the definition, functions and advantages of investment controlling and presented the reference model of its main subsystem – project portfolio controlling responsible for building the efficient capex project portfolio. Then, we developed the model of PRR; worked out the example of PRR’s scorecard and discussed the advantages of the PRR over the existing Risk assessment tools in project portfolio management.
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NEW2AN - Development of Project Risk Rating for Telecommunication Company
Lecture Notes in Computer Science, 2016Co-Authors: Sergei Grishunin, Svetlana SuloevaAbstract:We developed the project Risk Rating (PRR) for telecommunication companies. It provides qualitative Risk scores assessment of capital expenditures (capex) projects to rank them by severity of exposures, to check their fit into the company’s Risk profile and, ultimately, to combine projects into the efficient project portfolio with the lowest Risk given return. We discussed the definition, functions and advantages of investment controlling and presented the reference model of its main subsystem – project portfolio controlling responsible for building the efficient capex project portfolio. Then, we developed the model of PRR; worked out the example of PRR’s scorecard and discussed the advantages of the PRR over the existing Risk assessment tools in project portfolio management.
Francesco Paolucci - One of the best experts on this subject based on the ideXlab platform.
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The Potential for Risk Rating in Competitive Markets for Supplementary Health Insurance: An Empirical Analysis
Developments in Health Economics and Public Policy, 2010Co-Authors: Francesco PaolucciAbstract:Many countries are considering the option of reducing the share of mandatory basic health insurance (BI) and to increasingly rely on voluntary supplementary health insurance (SI) schemes to cover health care expenditures. In theory, competitive markets for SI tend to Risk-rated premiums. After discussing the determinants of Risk Rating in competitive SI markets, we estimate the potential for Risk Rating due to the transfer of benefits from BI to SI coverage. For this purpose, we simulate several scenarios in which benefits covered by BI are transferred to competitive markets for SI. We use a dataset from one of the largest insurers in the Netherlands, to calculate the potential premium range for SI resulting from this transfer. Our findings show that, by adding Risk-factors, the minimum SI premium decreases while the maximum increases. Moreover, we observe that Risk Rating primarily affects the maximum premium. The magnitude of the premium range is especially substantial for benefits such as medical devices and drugs. For these services the potential consequences of Risk Rating in terms of access to affordable insurance coverage may be considered not “socially acceptable”, since they result in high SI-premiums for certain Risk/income groups. Therefore, when transferring benefits from BI to SI policy makers should be aware of the implications for the affordability of insurance coverage.
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The potential premium range of Risk-Rating in competitive markets for supplementary health insurance
International Journal of Health Care Finance and Economics, 2009Co-Authors: Francesco Paolucci, Femmeke Prinsze, Pieter J. A. Stam, Wynand P. M. M. VenAbstract:In this paper, we simulate several scenarios of the potential premium range for voluntary (supplementary) health insurance, covering benefits which might be excluded from mandatory health insurance (MI). Our findings show that, by adding Risk-factors, the minimum premium decreases and the maximum increases. The magnitude of the premium range is especially substantial for benefits such as medical devices and drugs. When removing benefits from MI policymakers should be aware of the implications for the potential reduction of affordability of voluntary health insurance coverage in a competitive market.
Glenn D. Pederson - One of the best experts on this subject based on the ideXlab platform.
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Predictors of farm performance and repayment ability as factors for use in Risk‐Rating models
Agricultural Finance Review, 2003Co-Authors: Lyubov Zech, Glenn D. PedersonAbstract:This study investigates important factors that should be used by lenders in Risk‐Rating their farm customers. These factors predict actual farm performance and debt repayment ability. Linear and logistic regression models are used to identify the debt‐to‐asset ratio as a major predictor of repayment ability. In addition, the rate of asset turnover and family living expenses are strong predictors of farm performance. The results are tested over several time periods to verify the robustness of the predictors.
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predictors of farm performance and repayment ability as factors for use in Risk Rating models
Agricultural Finance Review, 2003Co-Authors: Lyubov Zech, Glenn D. PedersonAbstract:This study investigates important factors that should be used by lenders in Risk‐Rating their farm customers. These factors predict actual farm performance and debt repayment ability. Linear and logistic regression models are used to identify the debt‐to‐asset ratio as a major predictor of repayment ability. In addition, the rate of asset turnover and family living expenses are strong predictors of farm performance. The results are tested over several time periods to verify the robustness of the predictors.
