Life insurance is an important tool for protecting individuals and their families from the financial consequences of unexpected events such as illness, disability, or death. In Bangladesh, life- insurance premium pricing is primarily based on mortality rates, which are derived from life tables that provide statistical data on life expectancy and death rates based on age and gender. Using only mortality rates to determine premium pricing can have several disadvantages, and there are opportunities to leverage additional data and artificial intelligence (AI) to optimize pricing and improve outcomes both for insurers and the customers.
One of the major disadvantages of mortality rates-based pricing is its limited accuracy. Life tables provide an estimate of average life expectancy and mortality rates but cannot specify individual outcomes. Factors such as health status, lifestyle habits including smoking, occupation can greatly affect individual life expectancy, and life tables do not accurately reflect these variables. Absence of such critical information from the rate making exposes insurers to the adverse selection effect. Adverse selection occurs when individuals who are at higher risk of death are more likely to purchase life insurance, which can result in higher claims costs for insurers. This can ultimately lead to higher premiums for all customers, including those who are at lower risk of death.
Life tables are typically based on historical data, and do not necessarily account for changes in risk factors over time. This is a serious drawback. For example, as medical advancements occur, mortality rates may decrease, but life tables may not reflect this change. In addition to that these tables have limited applicability to non-life insurance because they are primarily designed for life-insurance products and may not be applicable to other types of insurance such as health or disability insurance.
To address these challenges and optimize life-insurance premium pricing, insurers can leverage additional data and AI to develop more sophisticated risk models. By incorporating data into the above-mentioned individual risk factors along with family medical history, individual claims history etc., insurers can develop more accurate risk profiles and determine premiums that are more closely aligned with an individual's unique risk level. This can result in more affordable premiums for low-risk individuals, while also ensuring that high-risk individuals pay premiums that are commensurate with their risk level.
The use of electronic health records (EHRs) is an example how insurers can leverage additional data to optimize pricing. The EHRs contain valuable data on an individual's health condition, medical history, and treatments, which can be used to develop more accurate risk profiles. By analyzing these data through AI algorithms, insurers can identify patterns and correlations between different risk factors and develop more accurate risk models that can improve existing pricing strategies. Insurers should partner local hospitals to develop integrated databases and leverage these resources.
Non-life insurers can use telematics data to improve their rate-making models. Telematics data are collected through sensors in vehicles and can provide valuable insights into an individual's driving habits, such as speed, acceleration, and braking. By analyzing this data insurers can develop more accurate risk profiles for auto insurance and decide premiums that are better aligned with an individual's driving behaviour.
Insurers have opportunities to use additional data sources, such as social- media data and credit scores, if available. By combining these data with mortality rates and other traditional risk factors, insurers can develop more sophisticated risk models to predict individual risk levels and determine personalized premiums matching unique risk profiles.
Mortality rates provide valuable insights into the probability of death based on age and gender. However, life- insurance-premium pricing relying only on mortality rates is not optimized and prone to adverse selection. By tapping into additional data sources and various techniques of AI, insurers can develop more advanced risk models that are better able to predict individual risk levels and determine customized premiums. This can result in more affordable rate for low-risk individuals, while also ensuring that high-risk individuals pay dividends commensurate with their risk levels. The use of additional data and AI has the potential to improve outcomes for insurers and their customers as well as increasing customer loyalty and satisfaction.
Dr. Nurur Rahman is the Founder and CEO of Somikoron, an AI-based insurtech startup in Bangladesh. Somikoron is paving the way for Bangladesh insurance, finance, and retails industries to be compatible with the Fourth Industrial Revolution.
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