scholarly journals Risk Assessment Models of the Companies Implementing R&D Projects

2019 ◽  
Vol 23 (1) ◽  
pp. 133-146
Author(s):  
V. В. Minasyan
Keyword(s):  
Risk Assessment ◽  
Risk Measure ◽  
High Growth ◽  
Assessment Model ◽  
Growth Potential ◽  
Triangular Distribution ◽  
Capital Investments ◽  
Case Scenario ◽  
Risk Assessment Models ◽  
Risk Appetite

Companies implementing R&D projects face their unique features. There is the need for large capital investments, long-term implementation, high growth potential, low probability of success, and diffculties in fnancing among them. Implementation of such projects is associated with high risks. This leads to underfunding as uncertain results deter investors. The problem of assessing the risks arising from the implementation of such projects has not yet been suffciently studied at the level of mathematical analysis models. The objective of the article is to develop a model allowing to explore the risks arising from implementing R&D projects. The author has developed a risk assessment model using the VaR measure modifed for this application. The formulas have been obtained to calculate this measure. They have been adjusted to simple analytical expressions assuming the balanced distribution of cash flow from the project, or triangular distribution. The model considers the most important causes of risks in R&D projects. It can be used in a real-case scenario if a preliminary risk assessment of a project is done before its implementation and a decision is made on risk-based implementation. Moreover, this methodology can be used to standardize the decision-making process for the R&D projects implementation considering the “risk appetite” using the VaR risk measure.

scholarly journals Potential Invasion Risk of Pet Traded Lizards, Snakes, Crocodiles, and Tuatara in the EU on the Basis of a Risk Assessment Model (RAM) and Aquatic Species Invasiveness Screening Kit (AS-ISK)

Diversity ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 164 ◽  
Author(s):  
Oldřich Kopecký ◽  
Anna Bílková ◽  
Veronika Hamatová ◽  
Dominika Kňazovická ◽  
Lucie Konrádová ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Common Species ◽  
Assessment Model ◽  
Thamnophis Sirtalis ◽  
Indigenous Species ◽  
The European Union ◽  
Risk Assessment Models ◽  
Species Invasiveness ◽  
Establishment Potential ◽  
Negative Impacts

Because biological invasions can cause many negative impacts, accurate predictions are necessary for implementing effective restrictions aimed at specific high-risk taxa. The pet trade in recent years became the most important pathway for the introduction of non-indigenous species of reptiles worldwide. Therefore, we decided to determine the most common species of lizards, snakes, and crocodiles traded as pets on the basis of market surveys in the Czech Republic, which is an export hub for ornamental animals in the European Union (EU). Subsequently, the establishment and invasion potential for the entire EU was determined for 308 species using proven risk assessment models (RAM, AS-ISK). Species with high establishment potential (determined by RAM) and at the same time with high potential to significantly harm native ecosystems (determined by AS-ISK) included the snakes Thamnophis sirtalis (Colubridae), Morelia spilota (Pythonidae) and also the lizards Tiliqua scincoides (Scincidae) and Intellagama lesueurii (Agamidae).

scholarly journals Credit Risk Assessment Models of Retail Microfinancing: The Case of a Malaysian National Savings Bank’s Branch

2020 ◽  
Vol 11 (3) ◽  
pp. 73
Author(s):  
Mazni Asrida Abdullah ◽  
Azlina Ahmad ◽  
Nor Azam Mat Nayan ◽  
Zubir Azhar ◽  
Abd-Razak Ahmad
Keyword(s):  
Risk Assessment ◽  
Default Risk ◽  
Credit Rating ◽  
Predictive Ability ◽  
Assessment Model ◽  
Accuracy Rate ◽  
Credit Score ◽  
Factors Associated ◽  
Risk Assessment Models ◽  
New Information

Internal ratings have been used by banks to evaluate the creditworthiness of their borrowers with diverse practices. This research aims to analyse the practice of assessing (or predicting) the credit performance of microfinancing loans of a Malaysian bank and to suggest how the existing performance of credit assessment model can be improved. Logistic regression was used to investigate the predictive ability of information on business operators’ management and accounting skills as factors to predict default risk of borrowers. The combination of these information formed the three (3) models that were used in the analysis. The accuracy rate of each model was then measured. A sample of respondents was selected among microfinance borrowers in a national savings bank’s branch in Malaysia. A total of 106 questionnaires were used for data analysis. The findings suggest that good credit rating, business experience, business financial and forecasting capability are factors associated with whether SMEs will default or not in their payments. The combination of credit score used currently by the bank and the new information produced by this research increases the bank’s ability to predict default.

Development and Implementation of a Liquid Pipeline Quantitative Risk Assessment Model

2014 ◽  
Author(s):  
Jerico Perez ◽  
David Weir ◽  
Caroline Seguin ◽  
Refaul Ferdous
Keyword(s):  
Risk Assessment ◽  
Risk Reduction ◽  
Assessment Tool ◽  
Assessment Model ◽  
Quantitative Risk Assessment ◽  
Pipeline System ◽  
Risk Assessment Model ◽  
Worst Case ◽  
Risk Appetite ◽  
Assessment Results

To the end of 2012, Enbridge Pipelines employed an in-house developed indexed or relative risk assessment algorithm to model its liquid pipeline system. Using this model, Enbridge was able to identify risk control or treatment projects (e.g. valve placement) that could mitigate identified high risk areas. A changing understanding of the threats faced by a liquid pipeline system and their consequences meant that the model changed year over year making it difficult to demonstrate risk reduction accomplished on an annual basis using a relative scoring system. As the development of risk management evolved within the company, the expectations on the model also evolved and significantly increased. For example, questions were being asked such as “what risk is acceptable and what risk is not acceptable?”, “what is the true risk of failure for a given pipe section that considers the likelihood of all threats applicable to the pipeline”, and “is enough being done to reduce these risks to acceptable levels?” To this end, starting in 2012 and continuing through to the end of 2013, Enbridge Pipelines developed a quantitative mainline risk assessment model. This tool quantifies both threat likelihood and consequence and offers advantages over the indexed risk assessment model in the following areas: • Models likely worst case (P90) rupture scenarios • Enables independent evaluation of threats and consequences in order to understand the drivers • Produces risk assessment results in uniform units for all consequence criteria and in terms of frequencies of failure for likelihood • Aggregates likelihood and consequence at varying levels of granularity • Uses the risk appetite of the organization and its quantification allows for the setting of defined high, medium, and low risk targets • Quantifies the amount of risk in dollars/year facilitating cost-benefit analyses of mitigation efforts and risk reduction activities • Grounds risk assessment results on changes in product volume-out and receptor sensitivity • Balances between complexity and utility by using enough information and data granularity to capture all factors that have a meaningful impact on risk Development and implementation of the quantitative mainline risk assessment tool has had a number of challenges and hurdles. This paper provides an overview of the approach used by Enbridge to develop its quantitative mainline risk assessment model and examines the challenges, learnings and successes that have been achieved in its implementation.

scholarly journals Risk factors, risk assessment, and prognosis in patients with gynecological cancer and thromboembolism

2019 ◽  
Vol 48 (4) ◽  
pp. 030006051989317
Author(s):  
Xindan Wang ◽  
Jing Huang ◽  
Zhao Bingbing ◽  
Shape Li ◽  
Li Li
Keyword(s):  
Risk Assessment ◽  
Gynecological Cancer ◽  
Area Under The Curve ◽  
Assessment Model ◽  
Receiver Operating Curve ◽  
Risk Level ◽  
Control Group ◽  
Risk Assessment Model ◽  
Vein Thrombosis ◽  
Risk Assessment Models

Objective This study aimed to investigate a suitable risk assessment model to predict deep vein thrombosis (DVT) in patients with gynecological cancer. Methods Data from 212 patients with gynecological cancer in the Affiliated Tumor Hospital of Guangxi Medical University were retrospectively analyzed. Patients were risk-stratified with three different risk assessment models individually, including the Caprini model, Wells DVT model, and Khorana model. Results The difference in risk level evaluated by the Caprini model was not different between the DVT and control groups. However, the DVT group had a significantly higher risk level than the control group with the Wells DVT or Khorana model. The Wells DVT model was more effective for stratifying patients in the DVT group into the higher risk level and for stratifying those in the control group into the lower risk level. Receiver operating curve analysis showed that the area under the curve of the Wells DVT, Khorana, and Caprini models was 0.995 ± 0.002, 0.642 ± 0.038, and 0.567 ± 0.039, respectively. Conclusion The Wells DVT model is the most suitable risk assessment model for predicting DVT. Clinicians could also combine the Caprini and Wells DVT models to effectively identify high-risk patients and eliminate patients without DVT.

Development of a Novel Preoperative Venous Thromboembolism Risk Assessment Model

2020 ◽  
Vol 86 (9) ◽  
pp. 1098-1105
Author(s):  
Eli Mlaver ◽  
Grant C. Lynde ◽  
Claire Gallion ◽  
John F. Sweeney ◽  
Jyotirmay Sharma
Keyword(s):  
Risk Assessment ◽  
Venous Thromboembolism ◽  
Data Entry ◽  
Predictive Ability ◽  
Clinical Decision ◽  
Assessment Model ◽  
Data Set ◽  
Improvement Project ◽  
Risk Assessment Models ◽  
American Society

Introduction Standardization of preoperative venous thromboembolism (VTE) risk assessment remains challenging due to variation in risk assessment models (RAMs) and the cumbersome workflow addition that most RAMs represent. We aimed to develop a parsimonious RAM that is automatable and actionable within the preoperative workflow. Methods We performed a case-controlled review of all 18 VTE cases reported over a 12-month period and 171 matched controls included in an institutional National Surgical Quality Improvement Project (NSQIP) data set. We examined the predictive value of the Caprini, Padua, and NSQIP RAMs. We identified the 5 most impactful risk factors in VTE development by contribution to the known RAMs. We compared the predictive ability of cancer, age, body mass index, black race, and American Society of Anesthesiologists Physical Status (ASA-PS) score, to the Caprini, Padua, and NSQIP RAMs for VTE outcomes. Finally, we evaluated concordance between each of the models. Results The Caprini Score was found to be 88.9% sensitive and 32.7% specific using a threshold of 5. The Padua score was found to be 61.1% sensitive and 47.4% specific using a threshold of 4. The novel 5-factor RAM was found to be 94.4% sensitive and 38.0% specific using a threshold of 4. The Caprini and Padua models were discordant in 26% of patients. Discussion Cumbersome manual data entry contributes to the ongoing challenge of standardized VTE risk assessment and prophylaxis. Universally documented information and patient demographics can be utilized to create clinical decision support tools that can improve the efficiency of perioperative workflow and improve the quality of care.

Feature Selection Optimization Model for Business Risk Assessment Model

2021 ◽  
pp. 51-64
Author(s):  
Noura Metawa ◽  
◽  
◽  
Mohamed Elhoseny
Keyword(s):  
Risk Assessment ◽  
Feature Selection ◽  
Financial Risk ◽  
Assessment Model ◽  
Classification Model ◽  
Financial Risks ◽  
Functional Link ◽  
Financial Firms ◽  
Risk Assessment Models ◽  
Assessment Performance

Financial risk assessment becomes a hot research topic among financial firms or companies to assess the financial status and thereby avoid future crises. Earlier studies have focused on statistical models for the assessment of financial risks and the recently developed machine learning (ML) models find useful to improve the assessment performance. In this aspect, this study introduces a novel Butterfly Optimization based Feature Selection with Classification Model for Financial Risk Assessment (BOFS-CFRA) technique. The proposed BOFS-CFRA technique involves pre-processing at the primary stage to get rid of unwanted data. In addition, K-means clustering approach is developed to group the financial data into clusters. Then, the BOFS technique is applied to choose the subset of features from the clustered data. Finally, the classification of financial risks takes place by the use of functional link neural network (FLNN). In order to ensure the enhanced performance of the BOFS-CFRA technique, a series of simulations were carried out and the results are inspected under various measures. The simulation outcome portrayed the supremacy of the BOFS-CFRA technique over the other financial risk assessment models in terms of several performance measures.

scholarly journals Growth and venture capital investment potential for university spin-offs in Hungary

2013 ◽  
Vol 7 (4-5) ◽  
pp. 31-38 ◽  
Author(s):  
Patrícia Becsky-Nagy
Keyword(s):  
Venture Capital ◽  
Capital Investment ◽  
Small Firms ◽  
Research Question ◽  
Academic Research ◽  
High Growth ◽  
Growth Potential ◽  
High Tech ◽  
Capital Investments ◽  
Venture Capital Investments

Venture backed spin-offs represent a low proportion of companies, even of innovative companies. The research question was, whether these companies have an important role in innovation and economic growth. I present the most important indicators of innovation in connection with entrepreneurship, the measures of start-ups, mainly the high-tech ones. I describe the position of venture capital industry nowadays, detailing the classical venture capital investments, targeting high-growth potential small firms, even university spin-offs. The study presents the results of a survey made as a counterpart of an academic research team, examining spin-offs, entrepreneurs and technology transfer in the most important Hungarian universities. I found that the most important obstacles of venture capital investments in high-tech spin-offs are the information gap between demand and supply side, the lack of entrepreneurs’ willingness to give up freedom in decision making, despite of low managerial skills. The low quality of financial environment is also an obstacle of the segment. JEL Codes: G24, M13

External validation of a risk assessment model for venous thromboembolism in the hospitalised acutely-ill medical patient (VTE-VALOURR)

2014 ◽  
Vol 112 (10) ◽  
pp. 692-699 ◽  
Author(s):  
Charles Mahan ◽  
Yang Liu ◽  
A. Graham Turpie ◽  
Jennifer Vu ◽  
Nancy Heddle ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Validation Cohort ◽  
External Validation ◽  
Medical Patient ◽  
Risk Groups ◽  
Assessment Model ◽  
Medical Patients ◽  
Control Cohort ◽  
Derivation Cohort ◽  
Risk Assessment Models

SummaryVenous thromboembolic (VTE) risk assessment remains an important issue in hospitalised, acutely-ill medical patients, and several VTE risk assessment models (RAM) have been proposed. The purpose of this large retrospective cohort study was to externally validate the IMPROVE RAM using a large database of three acute care hospitals. We studied 41,486 hospitalisations (28,744 unique patients) with 1,240 VTE hospitalisations (1,135 unique patients) in the VTE cohort and 40,246 VTE-free hospitalisations (27,609 unique patients) in the control cohort. After chart review, 139 unique VTE patients were identified and 278 randomly-selected matched patients in the control cohort. Seven independent VTE risk factors as part of the RAM in the derivation cohort were identified. In the validation cohort, the incidence of VTE was 0.20%; 95% confidence interval (CI) 0.18–0.22, 1.04%; 95%CI 0.88–1.25, and 4.15%; 95%CI 2.79–8.12 in the low, moderate, and high VTE risk groups, respectively, which compared to rates of 0.45%, 1.3%, and 4.74% in the three risk categories of the derivation cohort. For the derivation and validation cohorts, the total percentage of patients in low, moderate and high VTE risk occurred in 68.6% vs 63.3%, 24.8% vs 31.1%, and 6.5% vs 5.5%, respectively. Overall, the area under the receiver-operator characteristics curve for the validation cohort was 0.7731. In conclusion, the IMPROVE RAM can accurately identify medical patients at low, moderate, and high VTE risk. This will tailor future thromboprophylactic strategies in this population as well as identify particularly high VTE risk patients in whom multimodal or more intensive prophylaxis may be beneficial.

scholarly journals The Application of Current Proposed Venous Thromboembolism Risk Assessment Model for Ambulatory Patients With Cancer

2017 ◽  
Vol 24 (3) ◽  
pp. 429-433 ◽  
Author(s):  
Hikmat Abdel-Razeq ◽  
Asem Mansour ◽  
Salwa S. Saadeh ◽  
Mahmoud Abu-Nasser ◽  
Mohammad Makoseh ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Venous Thromboembolism ◽  
Stage Iv ◽  
Assessment Model ◽  
Risk Scores ◽  
Patients With Cancer ◽  
Risk Assessment Models ◽  
Ambulatory Patients ◽  
Treatment Paradigm ◽  
Outpatient Settings

Venous thromboembolism (VTE) is a commonly encountered problem in patients with cancer. In recent years, cancer treatment paradigm has shifted with most therapy offered in ambulatory outpatient settings. Excess of half VTEs in patients with cancer occur in outpatient settings without prior hospitalization, where current practice guidelines do not recommend routine prophylaxis. Risk assessment models (RAMs) for VTE in such patients were recently introduced. This study aims to assess the practical application of one of these models in clinical practice. Medical records and hospital electronic database were searched for patients with cancer having VTE. Known risk factors were collected, and risk assessment was done using the Khorana RAM. Over a 10-year period, 346 patients developed VTE in ambulatory settings. Median age was 57 and 59.0% were females. Lower extremities were involved in 196 (56.6%), while 96 (27.7%) had pulmonary embolism. Most (76.6%) patients had stage IV disease, only 9.0% had stage I or II disease. Only 156 (45.1%) patients were on active chemotherapy, for whom Khorana risk assessment score was calculated. In these patients, high risk was identified in 31 (19.9%) patients, while 81 (51.9%) had intermediate risk and 44 (28.2%) had low risk. No patients were on prophylaxis prior to VTE. Most ambulatory patients with cancer who developed VTE were not on chemotherapy, and many of those who were on active treatment had low Khorana risk scores. This illustrates the need to modify the model or develop a new one that takes into consideration this group of patients.

scholarly journals A Conceptual Model for Internet of Things Risk Assessment in Healthcare Domain with Deep Learning Approach

2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Mohd Nizam Zakaria ◽  
Nur Azaliah Abu Bakar ◽  
Hafiza Abas ◽  
Noor Hafizah Hassan
Keyword(s):  
Risk Assessment ◽  
Deep Learning ◽  
Internet Of Things ◽  
Risk Mitigation ◽  
Assessment Model ◽  
It Industry ◽  
Risk Assessment Models ◽  
Review Analysis ◽  
The Internet Of Things

The Internet of Things (IoT) has become a prevalent technology in the IT industry. One of the industries that can benefit extensively in this technology is healthcare. However, the healthcare IoT is still under debate with several studies suggesting it is lack of interoperability, security, and too much complexity. Even more, the risk involved in deploying it is still enormous. Many traditional risk assessment models are unable to provide a specific IoT risk guideline and specification, especially in the healthcare area. Thus, it is essential to understand the full extent of the IoT risk and how to manage its risk in the healthcare area. The risk management models, such as NIST SP 800-30, ISO/IEC 27005, OCTAVE, CRAMM, and EBIOS, which are among the leading and widely used in many areas and healthcare fields, have also been described. Besides, this paper includes a review of three IoT risk assessment models that are based on ABA-IDS, Deep Learning, and AHP-SVM. Based on the review analysis, we proposed a new enhanced healthcare IoT risk assessment model, which aims to provide a real-time monitoring and mitigating risks that incorporate the NIST SP 800-30 framework, ABA-IDS, and CNN deep learning. This shall constitute a better classification of each risk identified to find the best risk mitigation plan.

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