1. life tables to design life insurance policies.

1. Introduction

            Insurance is the main way for businesses and individuals to reduce the financial impact of a risk occurring (Lloyd’s, 2018). Insurance has many types but this report emphasizes the type of life insurance. Also, life insurance contracts help replace lost income if premature death occurs as it promises to pay a dollar benefit to a beneficiary upon the death of the insured person (Garman, Frogue, 2017).      

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now

            In addition, insurance risks are death, disability, longevity, illness, etc. Life insurance risks are mortality, disability, surrender, etc. In order to operate within a well-defined risk appetite, these technical risks are to be limited to a limit system (Koller, 2011).

            Running a business of any kind involves a certain amount of risk. Whether it is the risk of fire, the risk of damage to exported goods or the risk of natural disasters, all these incidents will have a financial impact on your business if they occur. This is risk meaning (Lloyd’s, 2018). Risk has also lots of types but this report emphasizes the type of model risk and model risk management in life insurance.

            These definitions enable to understand the reporting framework. Definitions make it easily explain model risk and model risk management in life insurance. Model risk is very important. It is the main risk in the insurance industry, especially on life insurance. Any deviations from expected claims and liabilities can be defined as model risk such as using improper life tables to design life insurance policies. The report proceeds within the framework of these model risk and model risk management, model risk sources and model risk control. All risks will explain the life table.

2. Model Risk and Model Risk Management

            At it is core, insurance seeks to bring some certainty into an uncertain world. So as to do this, the insurance industry makes models that simulate the future and populates these with assumptions based on the past. This process, by its nature, is subject to uncertainty and risk. How best to manage this risk is a key question for the insurance industry. These lead to manage model risk (PricewaterhouseCoopers PwC, 2015). In addition, the appearance of other mortality risk components should recognize. In detail, risks due to uncertainty in level as well as in trend of future mortality may heavily impact portfolio results. Special regard should be place when addressing long-term insurance products, for example life annuities (Pitacco, 2017).

Insurance agencies are in the matter of pricing and taking the risk, and models are regularly utilized and frequently basic as decision-support tools in that business. The protection business has a long convention of utilizing models of shifting multifaceted nature to drive decision making and manage risk (Chief Risk Officers Council CRO, 2016).

Risk management, and the improvement and use of models are two abilities that the insurance industry has more often than not performed well. Model risk management is not a new concept (Ernst & Young EY, 2014).

Put together, model risk can come up from different forms of errors or from inappropriate construction or use of the model. Model risk management is an important way to confirm that risk model on the requirement. In the light of these explaining, model risk is the most common and complicated problem in the insurance industry. In addition, it is impossible not mention about model risk management, while giving explanations model risk. These two issues can never be considered apart from each other.       

All explanations about model risk and model risk management are valid for life tables. Simply defines life table means is a table which shows, for a person at each age, what the probability is that they die before their next birthday. From this starting point, a number of statistics can be derived and thus also included in the table is the probability of surviving any particular year of age, the remaining life expectancy for people at different ages, the proportion of the original birth cohort still alive. A life table can be constructed for a country and an area on the basis of sex, occupation, race, etc. Life tables are usually constructed separately for men and for women because of their substantially different mortality rates. Moreover, insurance agencies utilize actuarial life tables to help price products and project future insured events. And life tables enter into a wide variety of annuity and life insurance computations. Life tables do not reflect % 100 truths. Confidence interval determines for this reason.  On top of everything, the life table is a model. It has risky, model risk. Model risk sources depend on quietly important factors and model risk control can develop and implement methods and techniques.




3. Model Risk Sources

Life table construction can differ among countries. These differences are such as religion, culture, demographic structure. Besides, these different show model risk sources by themselves. Also, model risk can differ among countries. This part explains the most common model risk sources the entire insurance industry, especially in life insurance industry.

Models can only estimate future results, and therefore they will never give answers that are %100 accurate. The reliability of results can likewise be influenced by the human error, including design flaws, incorrect calculations, out-of-date parameters, misunderstood or poorly communicated assumptions and results, poor data and the inappropriate application of a model (EY, 2014). These are operational factors.         

In developed countries decrease death and birth ratios, as born decreases longevity and life expectancy increases exact opposite. This situation devastates life table’s validity. These are external factors such as macroeconomics, health, education.

Reflection of the population to life tables correctly, overly significant problem model risk sources. This problem directly effects life insurance policy. Life insurance cannot determine accurately, indicate correct risk ratios. Inaccurate model outputs can also result in volatile, inefficient or inadequate. An unreliable model can produce wrong results, which can compromise strategic decision making and lead to financial losses or missed opportunities. Therefore, life insurance agencies lose its value and money.

In short model risk sources, the definition of the model may inadvertently remove analysis tools that introduce risk to the organization. The materiality of certain models may change over time. Much of the modeling performed at insurance agencies require a specialized set of technical skills, and validation of models requires a different mindset. Many models in use today were developed years ago and may be overlooked when model risk management activities focus on model development (EY, 2014).

Moreover, senior management and the board of directors may not be aware of the scope and expectations identified to the validation of a model. A truly independent review of the model may be hard to accomplish. Model validation may not address all areas of potential risk. The validity of the model changes over time. Models may be used incorrectly. Proper model documentation may not exist for older models that have been in place for years (EY, 2014).

4. Model Risk Control

Model risk control has to exist in insurance agencies. If it does not exist, become susceptible to misuse or errors that can have significant adverse consequences. Insurance agencies utilize complex models to support almost all critical business decisions. Financial models present risks at all insurance agencies and should be addressed as part of a comprehensive risk management program to protect an organization’s financial strength and reputation (EY, 2014).

In addition, among the risks which impact life insurance mortality risks deserve careful analysis and require the adoption of proper management solutions. When assessing the risk profile of a life insurer, riskiness arising from the behavior of mortality analyze via appropriate tools. In detail, the old approach to mortality modeling reject and replace by a new approach allowing for both process risk and uncertainty risk (Pitacco, 2017).


For a strong model risk control, model risk firstly identifies very-well. Model risk can mitigate throughout model risk control techniques. Therefore, model risk control techniques can reduce life table’s risk. This part explains model risk control all through life table’s construction.      

Hedging is a preferable method for model risk control. This is risk hedging. Reinsurance companies help to hedge risk for insurance agencies. Reinsurance is an extension of the concept of insurance, in that it passes on part of the risk for which the original insurer is liable. Due to the size and complexity of some risks, some insurers take out their own, additional insurance – as added protection for themselves. When insurers insure a risk again, it’s called reinsurance. Hedging risk enables to protect an insurer against very large claims and an international spread of risk (Lloyd’s, 2018). Also, hedging risk enables to take much more risk and create safe areas.          

In addition, back-testing is the most common technique for model risk control. Life table provides do back-testing. Back-testing calculates life table’s success in the past. Life tables are age-old. Insurers have to sure life tables are updated or not. Model risk, model risk sources and model risk control all of them continue to change in the global world. Be aware of these changing is necessary.


More than that, companies can improve different model risk control techniques for insurance agencies. They are all for the same purpose. For example, the audit company Ernst & Young presents its model risk control as divides three defense its structure; model owners, model governance and validation, internal audit (EY, 2014). The other audit company PricewaterhouseCoopers presents its model risk control as emphasis for insurers and divides three-part actuarial models, assumption setting, ongoing monitoring and benchmarking. (PwC, 2015).

Casualty Actuarial Society present and use as follows its cycle, assess, evaluate, manage, and measure (Stricker, Wang, Strommen, 2014).

Own Risk & Solvency Assessment (ORSA)            presents its model risk control cycle as do monitoring-reporting, management-implementation, identification, and assessment-measurement (Own Risk & Solvency Assessment ORSA, 2015). Model risk cannot avoid but it can reduce and mitigate. In the light of these explaining, it is possible to model risk control. Model risk control techniques are applicable, convenient, and reliable for life table’s construction.

This is simple scheme, ORSA how presents its model risk control cycle. ORSA and model risk, focus on life insurance (2012.)


5. Conclusion

            All in all, insurance, life insurance, risk, model risk, model risk management are the concepts this report. It is too important to know definition of these concepts very-well. They related each other. Therefore, model risk sources can determine easily and model risk control can manage adequately. These techniques enable to reach accurate life table’s usage.

            It is too hard to catch every changing in the global world. Awareness is more important than catching up something. Thus, when model risk sources know well-establish, model risk control improves much more efficiency and effective. Model risk cannot avoid but it can reduce by implementing model risk sources and model risk control.