scholarly journals Modeling of Motor Insurance Extreme Claims through Appropriate Statistical Distributions

2022 ◽  
Vol 40 (S1) ◽  
Author(s):  
V SELVAKUMAR ◽  
DIPAK KUMAR SATPATHI ◽  
P.T.V. PRAVEEN KUMAR ◽  
V. V HARAGOPAL
Keyword(s):  
Life Insurance ◽  
Control Strategies ◽  
Third Party ◽  
Distribution Model ◽  
Claim Amount ◽  
Insurance Risk ◽  
Probability Modeling ◽  
Motor Insurance ◽  
Programming Techniques ◽  
Actuarial Risk

In the area of insurance, probability modeling has a wide variety of applications. In life insurance, the compensation sum is calculated in advance and may often be estimated using actuarial techniques, while in motor insurance, the claim amount is generally not known in advance. In the insurance business, the improvement of actuarial risk control strategies is an essential technique for controlling insurance risk. Although an insurance company’s risk assessment about its solvency is a complex and detailed problem, its solution begins with statistical modeling of individual claims’ amounts. This article emphasizes the possible ways of obtaining a suitable probability distribution model that accurately explains insurance risks and how to use such a model for risk management purposes. For this reason, we have applied modern programming techniques and statistical software implemented the methods provided based on data on premium amounts of third-party motor insurance claims.

Note on Determination of Premium Rates for Motor Vehicles (Third Party Risks) Insurance in New Zealand

1972 ◽  
Vol 98 (2) ◽  
pp. 149-156
Author(s):  
J. E. Eriksen ◽  
E. J. Jones
Keyword(s):  
New Zealand ◽  
Life Insurance ◽  
Motor Vehicle ◽  
Third Party ◽  
Motor Vehicles ◽  
Motor Insurance ◽  
Premium Rates ◽  
Insurance Rates ◽  
Basic Ideas

The authors have advised on the level of motor vehicle (third party risks) insurance rates of premium in the circumstances under which that business is written in New Zealand and this paper records the approach taken. It has been prepared in the hope that, as no difficult mathematics are involved, the basic ideas may appeal both to actuaries and to persons other than actuaries who are interested in the transaction of non-life insurance. They are relevant not merely to third party motor insurance but also to non-life insurance generally in a situation where insurance is compulsory and the rates of premium are centrally controlled. In those circumstances more sophisticated techniques of deriving premium rates are less necessary.

scholarly journals Modeling and Predicting of Motor Insurance Claim Amount using Artificial Neural Network

2020 ◽  
Vol 8 (6) ◽  
pp. 4751-4757
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Life Insurance ◽  
Information Criteria ◽  
Insurance Companies ◽  
Claim Amount ◽  
Insurance Claim ◽  
Motor Insurance ◽  
Damage Claim

In India the insurance industry is in its growth stage. It consists of 58 insurance companies of which 24 in life and 34 are non-life insurance. The Non-life Insurance companies which cater to motor insurance business presently utilize different trend models to forecast paid claim amount. Motor Insurance Claim amount prediction is one of the most difficult tasks to accomplish in financial forecasting due to the complex nature of data points. The main objective of this study is to determine a reliable time series forecasting model to predict own damage (OD) claim amount of motor insurance data in India from 1981 to 2016. In this context, the annual time series claim data was collected and modeled by using the Generalized linear model (GLM), Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) method. The validation of the model has been done by comparison of predicted and actual values for the period of 36 years. Also, different types of possible models were evaluated using Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) for accuracy. The results showed that ANN outperformed other traditional time series models (GLM & ARIMA) for predicting the future own damage claim amount with a lesser residual error. Further, the outcome of these data analytics studies would help Insurance companies to have an idea about the expected future claim amounts with more accuracy. Thus, predicting the Motor insurance's own damage claim will help insurance companies to budget their future revenue

scholarly journals The Reduction of Initial Reserves Using the Optimal Reinsurance Chains in Non-Life Insurance

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1350
Author(s):  
Galina Horáková ◽  
František Slaninka ◽  
Zsolt Simonka
Keyword(s):  
Life Insurance ◽  
Continuous Time ◽  
Ruin Probability ◽  
Proportional Reinsurance ◽  
Insurance Risk ◽  
Optimal Reinsurance ◽  
Insurance Contracts ◽  
Time Period ◽  
Different Types ◽  
Capital Injections

The aim of the paper is to propose, and give an example of, a strategy for managing insurance risk in continuous time to protect a portfolio of non-life insurance contracts against unwelcome surplus fluctuations. The strategy combines the characteristics of the ruin probability and the values VaR and CVaR. It also proposes an approach for reducing the required initial reserves by means of capital injections when the surplus is tending towards negative values, which, if used, would protect a portfolio of insurance contracts against unwelcome fluctuations of that surplus. The proposed approach enables the insurer to analyse the surplus by developing a number of scenarios for the progress of the surplus for a given reinsurance protection over a particular time period. It allows one to observe the differences in the reduction of risk obtained with different types of reinsurance chains. In addition, one can compare the differences with the results obtained, using optimally chosen parameters for each type of proportional reinsurance making up the reinsurance chain.

The Risk of Birth

2021 ◽  
pp. 1-31
Author(s):  
Hannah Barker
Keyword(s):  
Maternal Mortality ◽  
Pregnant Women ◽  
Fifteenth Century ◽  
Life Insurance ◽  
Third Party ◽  
Potential Contribution ◽  
Reproductive Labor ◽  
Enslaved Women

Abstract Why did fifteenth-century Genoese slaveholders insure the lives of enslaved pregnant women? I argue that their assessment of the risks associated with childbirth reflected their views on the connection between slavery, property, and lineage. Genoese slaveholders saw the reproductive labor of enslaved women as a potential contribution to their lineage as well as their property. Because their children by enslaved women might become their heirs, Genoese slaveholders were inclined to worry about and seek protection against the risk of maternal mortality. In the context of the commercial revolution and the rise of third-party insurance, they developed life insurance for enslaved pregnant women to complement the fines already required of those who illegally impregnated enslaved women and thereby endangered their lives.

scholarly journals Problems in Motor Insurance — Claim Reserves

1962 ◽  
Vol 2 (1) ◽  
pp. 152-160 ◽  
Author(s):  
Norton E. Masterson
Keyword(s):  
Statistical Estimation ◽  
Third Party ◽  
Estimation Methods ◽  
Automobile Insurance ◽  
Insurance Claim ◽  
Motor Insurance ◽  
Safe Side ◽  
Aggregate Loss ◽  
Settlement Property ◽  
Image Position

In his comprehensive paper entitled “A General Survey of Problems Involved in Motor Insurance”, Dr. Carl Philipson includes remarks with respect to mathematical reserves. The purpose of this paper is to discuss a method of statistical estimation of Third Party Motor Insurance claim reserves. These methods can also be used for Car Damage Insurance, since reserve determination for these rapid settlement property coverages is simpler than for Third Party lines.Dr. Philipson mentions the two main purposes of claim reservesbalance sheet loss reserves which shall be estimated on the safe side for financial reasons, and those reserves needed for risk statistics. In this paper I shall confine my subject to aggregate loss reserves for financial statements and internal management operating reports.In this paper, it will be convenient to refer to both European and U.S. terminology for Motor Car and Automobile Insurance.The comparable terms are:The accident year is the important fiscal period underlying not only the statistical estimation methods discussed in this paper but it is also the basic grouping of accidents in the official reserve tests required in the Annual Statements of U.S. companies for casualty and property lines. An accident year embraces the entire population of claims incurred with accident dates in a particular calendar year, whether reported to the company in that year or subsequently (i.e., incurred /not reported).

scholarly journals Development of the first georeferenced map of Rhipicephalus (Boophilus) spp. in Mexico from 1970 to date and prediction of its spatial distribution

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Yazmin Alcala-Canto ◽  
Juan Antonio Figueroa-Castillo ◽  
Froylán Ibarra-Velarde ◽  
Yolanda Vera-Montenegro ◽  
María Eugenia Cervantes-Valencia ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Environmental Variables ◽  
Control Strategies ◽  
Integrated Control ◽  
Area Under The Curve ◽  
Entropy Method ◽  
Distribution Model ◽  
Economic Losses ◽  
Precipitation Seasonality ◽  
Epidemiological Risk

The tick genus Ripicephalus (Boophilus), particularly R. microplus, is one of the most important ectoparasites that affects livestock health and considered an epidemiological risk because it causes significant economic losses due, mainly, to restrictions in the export of infested animals to several countries. Its spatial distribution has been tied to environmental factors, mainly warm temperatures and high relative humidity. In this work, we integrated a dataset consisting of 5843 records of Rhipicephalus spp., in Mexico covering close to 50 years to know which environmental variables mostly influence this ticks’ distribution. Occurrences were georeferenced using the software DIVA-GIS and the potential current distribution was modelled using the maximum entropy method (Maxent). The algorithm generated a map of high predictive capability (Area under the curve = 0.942), providing the various contribution and permutation importance of the tested variables. Precipitation seasonality, particularly in March, and isothermality were found to be the most significant climate variables in determining the probability of spatial distribution of Rhipicephalus spp. in Mexico (15.7%, 36.0% and 11.1%, respectively). Our findings demonstrate that Rhipicephalus has colonized Mexico widely, including areas characterized by different types of climate. We conclude that the Maxent distribution model using Rhipicephalus records and a set of environmental variables can predict the extent of the tick range in this country, information that should support the development of integrated control strategies.

scholarly journals Reinsurance of Third Party Motor Insurance and Motor Insurance in Finland

1962 ◽  
Vol 2 (1) ◽  
pp. 174-176 ◽  
Author(s):  
Teivo Pentikäinen
Keyword(s):  
Point Of View ◽  
Third Party ◽  
Lump Sum ◽  
Legal Limits ◽  
Physical Injuries ◽  
Insurance Cover ◽  
Motor Insurance ◽  
Full Compensation ◽  
The Individual ◽  
Risk Limits

The need and extent of reinsurance of third party motor insurance depends fundamentally on the risk limits prescribed in the legislation of the country in question (and on the other hand the legal limits of the compulsory insurance may have been fixed with regard to the reasonable possibilities of the insurers getting reinsurance). There are two kinds of risk limits which are applied in different countries: total limits and individual limits. The former defines the maximum joint indemnity for an accident, paid to all claiments together, and the latter defines the maximum indemnity paid to each claiment separately. From the social point of view limits of this sort are not expedient, especially in regard to physical injuries. Owing to this total limit the indemnity for a single claiment can depend on the number of other claiments, which is quite inadequate from the point of view of the actual need to get insurance cover for injuries. The individual lump sum limit allows full compensation for slight injuries but can cut down the compensation for serious ones, which is an irrational method of settling an indemnity system. Owing to these risk limits motor car drivers may also be held responsible for the extra claims personally on the basis of civil (or criminal) law, which compels them to take an extra third party liability insurance (which often also has risk limits).

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