scholarly journals Aplicación del proceso de análisis jerárquico para la evaluación del riesgo de incendio en la industria colombiana

2021 ◽  
Vol 17 (33) ◽  
pp. 64-75
Author(s):  
Julian Uribe-Botero ◽  
Libardo Antonio Londoño-Ciro
Keyword(s):  
Occupational Safety ◽  
Fire Risk ◽  
Insurance Premium ◽  
Insurance Companies ◽  
Fire Departments ◽  
Analytic Hierarchy ◽  
Fire Insurance ◽  
Extra Charge ◽  
Factores De Riesgo ◽  
Detailed Assessment

La gestión de las emergencias hace parte fundamental de la seguridad y salud en el trabajo. Los incendios se constituyen como las emergencias más comunes en las industrias de nuestro país y en todo el mundo. Por medio del proceso de análisis multi atributo, específicamente el proceso de análisis jerárquico (AHP), los factores de riesgo fueron identificados en las investigaciones que se han realizado usando este método de análisis. El nivel de importancia de cada factor fue calculado por medio de un grupo de expertos que aplicaron el proceso de análisis jerárquico. Los resultados de la investigación pueden ser usados para tener una evaluación detallada el riesgo de incendio que permitiría a los inversionistas o propietarios tomar medidas para reducir las posibles pérdidas en caso de incendio; a las compañías de seguro realizar incrementos en las primas de riesgo basados en la evacuación del riesgo; a los departamentos de bomberos el nivel de cumplimiento de los requerimientos normativos.    Emergency management is a fundamental part of occupational safety and health. Fires are the most common emergencies in the industries of our country and throughout the world. Applying the multi-criteria analysis method, the investigations using the analytic hierarchy process (AHP) in fire risk assessment allowed the identification of fire risk factor. An experts group was consulted about the level of importance of fire risk factors in the fire risk evaluation, for these purpose each expert used the AHP. Results of research can be use to perform the detailed assessment of fire risk, allowing state owners and investors to carry out actions for reduce possible losses in case of fire. Insurance companies can perform the detailed assessment of fire risks on the object and to calculate a risk extra charge to an insurance premium. Fire departments can determine the compliance of a condition of object with requirements of regulations

scholarly journals Early Models Describing the Fire Insurance Risk

1979 ◽  
Vol 10 (3) ◽  
pp. 330-334 ◽  
Author(s):  
Paul Johansen
Keyword(s):  
Risk Premium ◽  
Fire Risk ◽  
Insurance Companies ◽  
Practical Case ◽  
Insurance Risk ◽  
Life Assurance ◽  
Fire Insurance ◽  
Italian Mathematicians ◽  
Risk Rate ◽  
Image Position

It has been known for generations that the fire risk rate increases with the size of the insured object in a similar way as the death rate increases with the age.Professor d'Addario and other Italian mathematicians have shown that statistical data often can be graduated by the formulas where S denotes the sum insured:In 1940, d'Addario) in a practical case found the values α = 0.78, β = 0.44. In 1956, Blandin and Depoid have used the same formulas. Although these formulas in practical cases often lead to good approximations, one can hardly say that a proper mathematical or physical model describing the behaviour of fires lies behind.About 1950, I worked with the statistics of a group of Danish fire insurance companies, in particular covering farm buildings. This investigation was organized by Gunnar Benktander. Our data confirmed the increase of the fire risk rate with the size of the buildings.In one special group: Farm houses with thatched roofs, this increase was so important that the risk premium was approximately proportionate to the square of the sum insured. The statistics fully justified the tariffing and we tried to construct a model describing and explaining the observed facts.The risk group in question was characterized by the overwhelming importance of total or practically total losses. Only a few per cent of the damages went to minor fires. When a fire breaks out in such a building, and reaches a certain slight extent, then it is not possible to save the building from total destruction. With sufficient approximation, we may say that only total losses occur (as in life assurance).

The Pioneer in American Life Insurance Marketing

1938 ◽  
Vol 12 (5) ◽  
pp. 65-75
Author(s):  
J. Owen Stalson
Keyword(s):  
United States ◽  
Eighteenth Century ◽  
North America ◽  
Life Insurance ◽  
General Public ◽  
Colonial America ◽  
The United States ◽  
Insurance Companies ◽  
Insurance Company ◽  
Fire Insurance

Colonial America gave little thought to life insurance selling. The colonists secured protection against marine risks from private underwriters, first in London, eventually at home. It has been asserted that Philadelphia had no fire insurance until 1752; Boston none before 1795. The first corporations formed in this country for insuring lives were those of the Presbyterian Ministers Fund (1759) and a similar company organized for the benefit of Episcopal ministers (1769). Neither of these corporations offered insurance to the general public. In the last decade of the eighteenth century many insurance companies were formed in the United States. At least five were chartered to underwrite life risks, but only one, The Insurance Company of North America, appears to have accepted any. There is no basis for saying that any of these early companies tried to sell life insurance.

scholarly journals Assessment of risks in the field of safety, quality and environment in post-harvest line

2016 ◽  
Vol 61 (Special Issue) ◽  
pp. S26-S36 ◽  
Author(s):  
M. Žitňák ◽  
K. Kollárová ◽  
M. Macák ◽  
M. Prístavková ◽  
M. Bošanský
Keyword(s):  
Failure Mode ◽  
Fire Safety ◽  
Occupational Safety ◽  
Fire Risk ◽  
Acceptable Level ◽  
Post Harvest ◽  
Before And After

The objective of this work is the evaluation of threats in the field of safety, fire safety, quality and environment in the post-harvest treatment of grain, with an emphasis put on pre-cleaning and drying of grain. The paper describes the technology of pre-cleaning and drying of grain. Risks for individual machines were evaluated using a failure mode and effects analysis (FMEA), which is used to evaluate the threats in terms of fire risk, occupational safety, the effect of machine on the quality of final product, and effect on the environment. We have proposed measures and solutions to eliminate or minimise the consequences of threats. Risk rates are evaluated before and after measures adopted. It can be stated based on the results that risk was successfully reduced to an acceptable level after adopting measures.

Fire Risk Assessment of Fire Retardant Polyurethane Thermal Insulation Materials for Exterior Walls of Buildings Based on Analytical Hierarchy Process

2013 ◽  
Vol 785-786 ◽  
pp. 191-198
Author(s):  
Li Jun Li ◽  
Feng Li ◽  
Ze Jiang Zhang
Keyword(s):  
Thermal Insulation ◽  
Evaluation Method ◽  
Fire Hazard ◽  
Fire Retardant ◽  
Fire Risk ◽  
Insulation Material ◽  
Test Results ◽  
Analytic Hierarchy ◽  
Rigid Foam ◽  
Hierarchy Process

Different flame retardant contents of polyurethane rigid foam (PRF) were prepared in this paper. Combustion performances of PRF were tested by cone calorimeter (CONE). Fire risk of PRF was evaluated based on Analytic Hierarchy Process (AHP). The evaluation results were compared with the traditional LOI results. It was found that the evaluation method based on AHP combined with CONE test results for evaluating fire hazard of thermal insulation material for exterior walls of buildings is more accurate and objective compared with the method of the traditional LOI.

Sharing the Risk

2021 ◽  
pp. 281-298
Author(s):  
Andrew C. A. Elliott
Keyword(s):  
Climate Change ◽  
Law Of Large Numbers ◽  
Insurance Market ◽  
Insurance Companies ◽  
Marine Insurance ◽  
Fire Insurance ◽  
Large Numbers ◽  
Mutual Insurance ◽  
The Face ◽  
Friendly Societies

Insurance makes use of the law of large numbers to mitigate the effects of risks on individuals by allowing them to be shared collectively. Early insurance arrangements arose as friendly societies and mutual insurance companies. Marine insurance has a long history and remains a major insurance market. Fire insurance provides compensation in the face of a capricious and frightening risk, but also invites fraudulent claims. Increasing amounts of information provide challenges for insurance underwriting: can there be too much information? The principle of insurance is that of averaging out of independent risks, but when risks are not independent, as may be the case when it comes to climate change, is there still any role for insurance?

Prioritizing Risk-level Factors in Comprehensive Automobile Insurance Management: A Hybrid Multi-criteria Decision-making Model

2020 ◽  
pp. 097215092093228
Author(s):  
Zahra Shams Esfandabadi ◽  
Meisam Ranjbari ◽  
Simone Domenico Scagnelli
Keyword(s):  
Insurance Market ◽  
Insurance Companies ◽  
Risk Level ◽  
Automobile Insurance ◽  
Two Phase ◽  
Analytic Hierarchy ◽  
Factors Affecting ◽  
Risk Levels ◽  
Fuzzy Delphi ◽  
The Individual

An efficient risk-level prediction for newly proposed insurance policies plays a significant role in the survival of companies in the highly competitive insurance market. In Iran, risk assessment in comprehensive automobile insurance, which is a part of motor insurance, is only based on the vehicle attributes without proper consideration of personal and behavioural characteristics of driver(s). As a result, pricing is unfair in most of the cases and this can put insurance companies in an unfavourable financial position due to attracting high-risk drivers instead of low-risk ones. In this scenario, to identify and prioritize important factors affecting risk levels and to move towards a fair ratemaking, a two-phase process based on fuzzy Delphi method (FDM) and fuzzy analytic hierarchy process (FAHP) is proposed in this research. Additionally, similarity aggregation method (SAM) is applied to combine the individual fuzzy opinions of the surveyed experts into a group fuzzy consensus opinion. The results of this empirical study contribute to the insurance market of Iran by proposing appropriate weighting of the relevant risk factors to support stakeholders and policymakers for assessing risks more accurately, as well as designing more effective databases and insurance proposal forms.

Farmers' Mutual Fire Insurance Companies in North Carolina

1941 ◽  
Vol 8 (2) ◽  
pp. 260
Author(s):  
Harvey Lebrun ◽  
Marc C. Leager
Keyword(s):  
North Carolina ◽  
Insurance Companies ◽  
Fire Insurance

scholarly journals Fire Risk Assessment Models Using Statistical Machine Learning and Optimized Risk Indexing

2020 ◽  
Vol 10 (12) ◽  
pp. 4199
Author(s):  
Myoung-Young Choi ◽  
Sunghae Jun
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Logistic Regression ◽  
Prediction Model ◽  
Fire Risk ◽  
Fire Occurrence ◽  
Statistical Machine Learning ◽  
Fire Insurance ◽  
Comparison Results ◽  
Fire Risk Assessment

It is very difficult for us to accurately predict occurrence of a fire. But, this is very important to protect human life and property. So, we study fire hazard prediction and evaluation methods to cope with fire risks. In this paper, we propose three models based on statistical machine learning and optimized risk indexing for fire risk assessment. We build logistic regression, deep neural networks (DNN) and fire risk indexing models, and verify performances between proposed and traditional models using real investigated data related to fire occurrence in Korea. In general, fire prediction models currently in use do not provide satisfactory levels of accuracy. The reason for this result is that the factors affecting fire occurrence are very diverse and frequency of fire occurrence is very sparse. To improve accuracy of fire occurrence, we first build logistic regression and DNN models. In addition, we construct a fire risk indexing model for a more improved model of fire prediction. To illustrate comparison results between our research models and current fire prediction model, we use real fire data investigated in Korea between 2011 to 2017. From the experimental results of this paper, we can confirm that accuracy of prediction by the proposed method is superior to the existing fire occurrence prediction model. Therefore, we expect the proposed model to contribute to evaluating the possibility of fire risk in buildings and factories in the field of fire insurance and to calculate the fire insurance premium.

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