scholarly journals Earthquake Catastrophe Risk Modeling, Application to the Insurance Industry: Unknowns and Possible Sources of Bias in Pricing

2021 ◽  
pp. 239-274
Author(s):  
M. Kohrangi ◽  
A. N. Papadopoulos ◽  
S. R. Kotha ◽  
D. Vamvatsikos ◽  
P. Bazzurro
Keyword(s):  
Risk Assessment ◽  
Insurance Industry ◽  
Earthquake Risk ◽  
Risk Modeling ◽  
Portfolio Risk ◽  
Building Vulnerability ◽  
Risk Assessment Models ◽  
Future Events ◽  
Superior Approach ◽  
Traditional Approaches

AbstractMathematical risk assessment models based on empirical data and supported by the principles of physics and engineering have been used in the insurance industry for more than three decades to support informed decisions for a wide variety of purposes, including insurance and reinsurance pricing. To supplement scarce data from historical events, these models provide loss estimates caused to portfolios of structures by simulated but realistic scenarios of future events with estimated annual rates of occurrence. The reliability of these estimates has evolved steadily from those based on the rather simplistic and, in many aspects, semi-deterministic approaches adopted in the very early days to those of the more recent models underpinned by a larger wealth of data and fully probabilistic methodologies. Despite the unquestionable progress, several modeling decisions and techniques still routinely adopted in commercial models warrant more careful scrutiny because of their potential to cause biased results. In this chapter we will address two such cases that pertain to the risk assessment for earthquakes. With the help of some illustrative but simple applications we will first motivate our concerns with the current state of practice in modeling earthquake occurrence and building vulnerability for portfolio risk assessment. We will then provide recommendations for moving towards a more comprehensive, and arguably superior, approach to earthquake risk modeling that capitalizes on the progress recently made in risk assessment of single buildings. In addition to these two upgrades, which in our opinion are ready for implementation in commercial models, we will also describe an enhancement in ground motion prediction that will certainly be considered in the models of tomorrow but is not yet ready for primetime. These changes are implemented in example applications that highlight their importance for portfolio risk assessment. Special consideration will be given to the potential bias in the Average Annual Loss estimates, which constitutes the foundation of insurance and reinsurance policies’ pricing, that may result from the application of the traditional approaches.

scholarly journals A review of fuzzy risk assessment models for construction projects

2012 ◽  
Vol 20 (3) ◽  
pp. 35-40 ◽  
Author(s):  
Pejman Rezakhani
Keyword(s):  
Risk Assessment ◽  
Construction Projects ◽  
Literature Survey ◽  
Extensive Literature ◽  
Risk Modeling ◽  
Modeling And Analysis ◽  
Analytic Methods ◽  
Risk Assessment Models ◽  
New Models ◽  
Fuzzy Risk Assessment

AbstractRisk modeling and analysis is one of the most important stages in a project`s success.There are many approaches for risk assessment, and an investigation of existing methodshelps in developing new models. This paper is an extensive literature survey in riskmodeling and analytic methods with a main focus on fuzzy risk assessment.

VTE risk assessment in cancer

2016 ◽  
Vol 07 (01) ◽  
pp. 20-25
Author(s):  
I. Pabinger ◽  
C. Ay
Keyword(s):  
Risk Assessment ◽  
Cancer Patients ◽  
Medical Oncology ◽  
Clinical Importance ◽  
Cancer Surgery ◽  
Ambulatory Setting ◽  
Oncology Patients ◽  
Related Factors ◽  
Risk Assessment Models ◽  
Increased Risk

SummaryVenous thromboembolism (VTE) in patients with cancer is associated with an increased morbidity and mortality, and its prevention is of major clinical importance. However, the VTE rates in the cancer population vary between 0.5% - 20%, depending on cancer-, treatment- and patient-related factors. The most important contributors to VTE risk are the tumor entity, stage and certain anticancer treatments. Cancer surgery represents a strong risk factor for VTE, and medical oncology patients are at increased risk of developing VTE, especially when receiving chemotherapy or immunomodulatory drugs. Also biomarkers have been investigated for their usefulness to predict risk of VTE (e.g. elevated leukocyte and platelet counts, soluble P-selectin, D-dimer, etc.). In order to identify cancer patients at high risk of VTE and to improve risk stratification, risk assessment models have been developed, which contain both clinical parameters and biomarkers. While primary thromboprophylaxis with lowmolecular- weight-heparin (LMWH) is recommended postoperatively for a period of up to 4 weeks after major cancer surgery, the evidence is less clear for medical oncology patients. Thromboprophylaxis in hospitalized medical oncology patients is advocated, and is based on results of randomized controlled trials which evaluated the efficacy and safety of LMWH for prevention of VTE in hospitalized medically ill patients. In recent trials the benefit of primary thromboprophylaxis in cancer patients receiving chemotherapy in the ambulatory setting has been investigated. However, at the present stage primary thromboprophylaxis for prevention of VTE in these patients is still a matter of debate and cannot be recommended for all cancer outpatients.

Risk assessment models with functionally different influences on natural and technical systems

2020 ◽  
Vol 89 ◽  
pp. 8-19
Author(s):  
V. A. Minaev ◽  
◽  
N. G. Topolsky ◽  
A. O. Faddeev ◽  
R. O. Stepanov ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Differential Equations ◽  
Research Area ◽  
Practical Implementation ◽  
Model Assessment ◽  
Mathematical Functions ◽  
Effective Response ◽  
Risk Assessment Models ◽  
Technical Systems ◽  
Material Values

Introduction. The complex combination of natural and technogenic factors that lead to dangerous threats to the health and life of the population, as well as to material values, creates a need to develop special mathematical models for risk assessment in the relevant territories. Herewith it is important to take into account the significant differences between these factors. The new areas of research are models that describe natural and technogenic risks using differential equations that reflect different types of functions. The article presents the development of this research area. Goals and objectives. The goal of the article is to create a model for risk assessment in natural and technical systems (PTS), based on taking into account the influences of different natural and technogenic factors on them. Objectives include justification, construction and practical implementation of the mathematical model of risk assessment in the form of differential equations system. Methods include interpretation of the considered influences on PTS in terms of risks and assessment of the dynamic interaction of natural and technogenic factors in the form of inhomogeneous differential equations. Results and discussion. Solutions for models of assessing complex natural and technogenic risks in relation to two cases that differ in NTS are found: functionally different external natural and technogenic influences on PTS, which are understood as their type, in which the effects of both natural and technogenic factors are described by different mathematical functions. Conclusions. The first model considers parabolic (reflecting threats whose intensity gradually decreases with distance from the epicenter) and linear types of influences (reflecting sudden threats). The second model considers parabolic and hyperbolic (reflecting threats, the intensity of which decreases sharply over time) types of influences. It is concluded that it is necessary to create a special computer album of complex influences on the PTS in order to prevent "replay" of various situations and develop the most effective response to emerging dangers from the EMERCOM units and other structures. Key words: model, assessment, natural and technogenic risks, functionally different influences, counteraction, EMERCOM units.

A MODELING SYSTEM FOR CLIMATE CHANGE RISK ASSESSMENT, MANAGEMENT AND HEDGING IN COASTAL AREAS

2017 ◽  
Author(s):  
Sergei Soldatenko ◽  
Sergei Soldatenko ◽  
Genrikh Alekseev ◽  
Genrikh Alekseev ◽  
Alexander Danilov ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Coastal Areas ◽  
Mitigation Strategies ◽  
System Structure ◽  
The Arctic ◽  
Risk Modeling ◽  
Wide Range ◽  
Adverse Weather ◽  
The Impact

Every aspect of human operations faces a wide range of risks, some of which can cause serious consequences. By the start of 21st century, mankind has recognized a new class of risks posed by climate change. It is obvious, that the global climate is changing, and will continue to change, in ways that affect the planning and day to day operations of businesses, government agencies and other organizations and institutions. The manifestations of climate change include but not limited to rising sea levels, increasing temperature, flooding, melting polar sea ice, adverse weather events (e.g. heatwaves, drought, and storms) and a rise in related problems (e.g. health and environmental). Assessing and managing climate risks represent one of the most challenging issues of today and for the future. The purpose of the risk modeling system discussed in this paper is to provide a framework and methodology to quantify risks caused by climate change, to facilitate estimates of the impact of climate change on various spheres of human activities and to compare eventual adaptation and risk mitigation strategies. The system integrates both physical climate system and economic models together with knowledge-based subsystem, which can help support proactive risk management. System structure and its main components are considered. Special attention is paid to climate risk assessment, management and hedging in the Arctic coastal areas.

Hysteresis effect on earthquake risk assessment of moment resisting frame structures

2021 ◽  
Vol 242 ◽  
pp. 112532
Author(s):  
Zhenhua Huang ◽  
Liping Cai ◽  
Yashica Pandey ◽  
Yong Tao ◽  
William Telone
Keyword(s):  
Risk Assessment ◽  
Hysteresis Effect ◽  
Frame Structures ◽  
Earthquake Risk ◽  
Moment Resisting Frame ◽  
Moment Resisting ◽  
Earthquake Risk Assessment

scholarly journals A practical approach for evaluating the strength of knowledge supporting risk assessment models

2020 ◽  
Vol 124 ◽  
pp. 104596 ◽  
Author(s):  
Tasneem Bani-Mustafa ◽  
Zhiguo Zeng ◽  
Enrico Zio ◽  
Dominique Vasseur
Keyword(s):  
Risk Assessment ◽  
Practical Approach ◽  
Risk Assessment Models

On the treatment of uncertainty in seismic vulnerability and portfolio risk assessment

2017 ◽  
Vol 47 (1) ◽  
pp. 87-104 ◽  
Author(s):  
Luis Sousa ◽  
Vitor Silva ◽  
Mário Marques ◽  
Helen Crowley
Keyword(s):  
Risk Assessment ◽  
Seismic Vulnerability ◽  
Portfolio Risk

Layers of Bias: A Unified Approach for Understanding Problems With Risk Assessment

2018 ◽  
Vol 46 (2) ◽  
pp. 185-209 ◽  
Author(s):  
Laurel Eckhouse ◽  
Kristian Lum ◽  
Cynthia Conti-Cook ◽  
Julie Ciccolini
Keyword(s):  
Risk Assessment ◽  
Criminal Justice ◽  
Justice System ◽  
Quantitative Risk Assessment ◽  
Risk Scores ◽  
Risk Models ◽  
Risk Assessment Models ◽  
Final Layer ◽  
The Supreme Court ◽  
Using Data

Scholars in several fields, including quantitative methodologists, legal scholars, and theoretically oriented criminologists, have launched robust debates about the fairness of quantitative risk assessment. As the Supreme Court considers addressing constitutional questions on the issue, we propose a framework for understanding the relationships among these debates: layers of bias. In the top layer, we identify challenges to fairness within the risk-assessment models themselves. We explain types of statistical fairness and the tradeoffs between them. The second layer covers biases embedded in data. Using data from a racially biased criminal justice system can lead to unmeasurable biases in both risk scores and outcome measures. The final layer engages conceptual problems with risk models: Is it fair to make criminal justice decisions about individuals based on groups? We show that each layer depends on the layers below it: Without assurances about the foundational layers, the fairness of the top layers is irrelevant.

scholarly journals Validation of Risk Assessment Models for Predicting the Incidence of Breast Cancer in Korean Women

2014 ◽  
Vol 17 (3) ◽  
pp. 226 ◽  
Author(s):  
Jun Won Min ◽  
Myung-Chul Chang ◽  
Hae Kyung Lee ◽  
Min Hee Hur ◽  
Dong-Young Noh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Korean Women ◽  
Risk Assessment Models
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