Risk defined as it is a pecuniary benefit

management challenges are the one important 
issues facing insurance companies.Risk is a double-edged knife for
insurance companies.There are several kinds of risk in insurance companies like
model risk.This essay discusses model risk in life insurances,what are sources
of the model risk in life insurances,why does model risk arise and how can
control or avoid model risk.

            2. Definitions


            Definiton of model risk is risk of
loss arising from valuing financial instruments with a model that is inaccurate(Model
risk, 2011).In other words,the model that used to calculate a firm’s market
risk does not implement the tasks.Model risk is the main risk in insurance
sector. Any
deviations from expected claims and liabilities can be defined as model risk. Model risk may also
become susceptible to misuse or errors that can have significant adverse
consequences, including financial.Models are used for core financial functions
such as financial reporting, where any oversight or errors can result in
financial restatements, which can lead to the loss of investor, regulator and
policyholder confidence Inaccurate model outputs can also result in volatile,
inefficient or inadequate capital or reserve requirements required by local
insurance regulators or accounting boards(Lebel & Gagnon, 2015).

            Life insurance can be defined as it
is a pecuniary benefit to the survivors of insured person upon his/her death.That
is, it
is 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 life
insurance is provide a protection of financial comfort for his/her family after
policyholder dies. Life
insurance companies estimate insurance premiums through life tables.

            Life tables ,which are one of the
oldest tools of demographic analysis,are tables detailing the mortality
probabilities and other statistics such as life expectancy at each age and
survival times of the population at all ages. Life tables are also known as
mortality tables.Life tables are constructed by following  a cohort from birth to death.It can also be
constructed from vital registration. Widely,they are constructed for age,
sexuality, ethnic groups and occupational groups.

2.1 Sources
of Model Risk


examine relationship between model risk and life insurance.The most important
model risk in life insurance is life tables because life insurance companies need
life tables to estimate insurance premiums.There are a lot of factors that
could be model risk for life tables.

            First-one is data problems.Data that
is used for life tables may not reflect the population. Parameters are estimated
from an observed sample.Parameter uncertainty results from differences between
that sample and the population(Venter & Sahasrabuddhe, 2012). For example,Turkey
has used a life table that was developed for U.S.A because there has not been a
life table about Turkey population.As a result,Turkey has taken model risk.The
table does not reflect characteristics of Turkey population because the life
table not only has gender(female,male) but also classification between white
and black.

            Secondly,changing factors over time
may lead to occurrence of model risk. Many models need the future value of some
volatility or correlation.This value is often based on historical data but
history may not providea good estimate of future value, and historical values
may themselves be unstable and vary strongly with the sampling period(Derman,
1996).For instance,Innovations in healthcare  can change factors such as birth and death
rate.Changes in the effect size of factors will have a remarkable impact on the
applicability of a model.Also,sudden changing factors such as  an economic crisis,earthquake and so on can
affect  the applicability of a model.

            Thirdly,model misspecification also  leads to occurrence of model risk. Model misspecification is
the risk that the wrong model is being estimated and applied(Venter &
Sahasrabuddhe, 2012).A model might be misspecified if important variables have
been omitted and chosen a wrong functional form.For example, this is the risk that we
use an exponential model when the phenomenon follows a Pareto distribution(Venter
& Sahasrabuddhe, 2012).

            Last but not least, a model could be built
correctly but it might be used for the wrong task.H?rsa(2012) mentioned that if
 we assume that we have chosen a correct
model and computed a correct solution under that model,there is still  the risk that the model results will be used
inappropriately.This has often been a problem in the modern history of
matematical finance where those who utilize models and their results fail to
understand their assumptions and limitations.Therefore,even though a model is a
correct model or solved correctly,it has the potential to cause problems.

2.2 Model
Risk Management


explaining  sources of model risk that could
affect life tables, need to discuss how to avoid model risk because all kinds
of model risk in life tables lead to estimate incorrect insurance premiums.Model
risk can not be controlled or eliminated at all  but at least be aware of meaning of the risk
and souces of this risk.As a result,life insurance companies should adjust
confidence level and tolerance  to
estimate insurance premiums in terms of the risk.Miller(2014) mention that even
with skilled modeling and robust validation, model risk can not be eliminated,
so other tools should be used to manage model risk effectively. Among these are
establishing limits on model use, monitoring model performance, adjusting or
revising models over time, and supplementing model results with other analysis
and information.There are two ways that mitigate model risk.

            Firstly,back testing is comparing
actual results for a defined period to the results forecasted by a model for
the same period in order to evaluate accuracy of the model’s
predictiveness.Back testing is an exercise that compares the actual outcome
with model forecasts during a defined period, a period of time that was not
used to develop the methodology(Lubansky, 2015).The evaluation of value at risk
is an example of back testing.In this example, actual profit and
loss is compared with a model forecast loss distribution.In general,the
comparison is performed  using
statistical confidence intervals around the model forecasts.However, using back
testing could be harder for life tables.It takes a long time when using back
testing for life tables because there are 80 years old life tables.It means
that there is a massive historical data and it is not easy to examine all of
them.Also,life tables become old after  a
certain year because of increasing the average life span.As a result,the data
could 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 purchasing
insurance polices       from other
insurers to limit  the total loss the
original insurer would experience in case of disaster .The premium paid by the
insured is typically shared by all of the insurance companies involved(Reassurance,
2008).That is to say insurance companies insure their own risk.Consequently,reassurance
encourages insurance companies to take a risk since reassurance gives the
insurer more security.