Riskmanagement challenges are the one important issues facing insurance companies.Risk is a double-edged knife forinsurance companies.There are several kinds of risk in insurance companies likemodel risk.This essay discusses model risk in life insurances,what are sourcesof the model risk in life insurances,why does model risk arise and how cancontrol or avoid model risk.            2.

Definitions             Definiton of model risk is risk ofloss arising from valuing financial instruments with a model that is inaccurate(Modelrisk, 2011).In other words,the model that used to calculate a firm’s marketrisk does not implement the tasks.Model risk is the main risk in insurancesector. Anydeviations from expected claims and liabilities can be defined as model risk. Model risk may alsobecome susceptible to misuse or errors that can have significant adverseconsequences, including financial.Models are used for core financial functionssuch as financial reporting, where any oversight or errors can result infinancial restatements, which can lead to the loss of investor, regulator andpolicyholder confidence Inaccurate model outputs can also result in volatile,inefficient or inadequate capital or reserve requirements required by localinsurance regulators or accounting boards(Lebel & Gagnon, 2015).

            Life insurance can be defined as itis a pecuniary benefit to the survivors of insured person upon his/her death.Thatis, itis a contract between an insurance policy holder and an insurance company,where the insurer promises to pay a sum of money in exchange for a premium,upon the death of an insured person or after a set period(“Life insurance,”n.d.

).In general,payment is made at time of policyholder death.The aim of lifeinsurance is provide a protection of financial comfort for his/her family afterpolicyholder dies. Lifeinsurance companies estimate insurance premiums through life tables.            Life tables ,which are one of theoldest tools of demographic analysis,are tables detailing the mortalityprobabilities and other statistics such as life expectancy at each age andsurvival times of the population at all ages. Life tables are also known asmortality tables.

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Life tables are constructed by following  a cohort from birth to death.It can also beconstructed from vital registration. Widely,they are constructed for age,sexuality, ethnic groups and occupational groups.2.1 Sourcesof Model Risk             Let’sexamine relationship between model risk and life insurance.The most importantmodel risk in life insurance is life tables because life insurance companies needlife tables to estimate insurance premiums.There are a lot of factors thatcould be model risk for life tables.            First-one is data problems.

Data thatis used for life tables may not reflect the population. Parameters are estimatedfrom an observed sample.Parameter uncertainty results from differences betweenthat sample and the population(Venter & Sahasrabuddhe, 2012). For example,Turkeyhas used a life table that was developed for U.S.A because there has not been alife table about Turkey population.As a result,Turkey has taken model risk.Thetable does not reflect characteristics of Turkey population because the lifetable not only has gender(female,male) but also classification between whiteand black.

            Secondly,changing factors over timemay lead to occurrence of model risk. Many models need the future value of somevolatility or correlation.This value is often based on historical data buthistory may not providea good estimate of future value, and historical valuesmay themselves be unstable and vary strongly with the sampling period(Derman,1996).For instance,Innovations in healthcare  can change factors such as birth and deathrate.Changes in the effect size of factors will have a remarkable impact on theapplicability of a model.Also,sudden changing factors such as  an economic crisis,earthquake and so on canaffect  the applicability of a model.            Thirdly,model misspecification also  leads to occurrence of model risk.

Model misspecification isthe risk that the wrong model is being estimated and applied(Venter &Sahasrabuddhe, 2012).A model might be misspecified if important variables havebeen omitted and chosen a wrong functional form.For example, this is the risk that weuse an exponential model when the phenomenon follows a Pareto distribution(Venter& Sahasrabuddhe, 2012).            Last but not least, a model could be builtcorrectly but it might be used for the wrong task.

H?rsa(2012) mentioned that if we assume that we have chosen a correctmodel and computed a correct solution under that model,there is still  the risk that the model results will be usedinappropriately.This has often been a problem in the modern history ofmatematical finance where those who utilize models and their results fail tounderstand their assumptions and limitations.Therefore,even though a model is acorrect model or solved correctly,it has the potential to cause problems.2.2 ModelRisk Management Afterexplaining  sources of model risk that couldaffect life tables, need to discuss how to avoid model risk because all kindsof model risk in life tables lead to estimate incorrect insurance premiums.Modelrisk can not be controlled or eliminated at all  but at least be aware of meaning of the riskand souces of this risk.As a result,life insurance companies should adjustconfidence level and tolerance  toestimate insurance premiums in terms of the risk.

Miller(2014) mention that evenwith skilled modeling and robust validation, model risk can not be eliminated,so other tools should be used to manage model risk effectively. Among these areestablishing limits on model use, monitoring model performance, adjusting orrevising models over time, and supplementing model results with other analysisand information.There are two ways that mitigate model risk.            Firstly,back testing is comparingactual results for a defined period to the results forecasted by a model forthe same period in order to evaluate accuracy of the model’spredictiveness.Back testing is an exercise that compares the actual outcomewith model forecasts during a defined period, a period of time that was notused to develop the methodology(Lubansky, 2015).The evaluation of value at riskis an example of back testing.

In this example, actual profit andloss is compared with a model forecast loss distribution.In general,thecomparison is performed  usingstatistical confidence intervals around the model forecasts.However, using backtesting could be harder for life tables.It takes a long time when using backtesting for life tables because there are 80 years old life tables.It meansthat there is a massive historical data and it is not easy to examine all ofthem.Also,life tables become old after  acertain year because of increasing the average life span.As a result,the datacould be unstable.

It means that there could be a problem  about reliability of back testing.            Secondly,reassurance can be defined as it occurs  when multiple  insurance companies share or transfer  risk with another companies by purchasinginsurance polices       from otherinsurers to limit  the total loss theoriginal insurer would experience in case of disaster .The premium paid by theinsured is typically shared by all of the insurance companies involved(Reassurance,2008).That is to say insurance companies insure their own risk.Consequently,reassuranceencourages insurance companies to take a risk since reassurance gives theinsurer more security.