Mastering the Underlying Mortality Assumption: A Comprehensive Guide

This article delves into the concept of underlying mortality assumptions, their applications in insurance and pension estimates, and the critical role actuaries play in modeling these projections.

Underlying mortality assumptions are projections of expected death rates used by actuaries to estimate insurance premiums and pension obligations. These estimates rely on mortality tables, which are statistical representations of expected annual mortality rates.

Due to their critical importance, actuaries must adhere to guidelines set by pension and insurance regulators to determine the appropriate assumptions, often referred to as mortality assumptions.

Key Insights

  • Underlying mortality assumptions model projections of expected death rates used by actuaries.
  • They are essential for estimating insurance premiums and pension obligations.
  • These assumptions are built on mortality tables, statistical records of expected annual mortality rates.

Understanding the Underlying Mortality Assumption

The underlying mortality assumption is pivotal for estimating life expectancies, thereby affecting the cost of insurance for insurers and long-term obligations for pension funds. A low mortality assumption could lead a life insurer to underestimate the actual cost of insurance, potentially leading to higher payouts in death benefit claims than anticipated.

On the other hand, if the mortality assumption is too high, the actuary might underestimate the life expectancies of pension plan members, affecting the financial planning for the pension fund.

For most people, considering death is uncomfortable, but for life insurers and pension administrators, it’s a crucial aspect. A proficient actuary understands that mortality rates vary at different life stages, with mortality at birth differing significantly from that in advanced age.

Special Considerations

The Centers for Disease Control provided data for 2020 shedding light on mortality statistics. Key points include:

  • The death rate per 100,000 population was 835.4.
  • Life expectancy at birth was 77 years.
  • Infant mortality rate was 541.9 deaths per 100,000 live births.

Leading causes of death included:

  1. Heart disease: 168.2 deaths per 100,000 population
  2. Cancer: 144.1 deaths per 100,000 population
  3. COVID-19: 85.0 deaths per 100,000 population
  4. Unintentional injuries (accidents): 57.6 deaths per 100,000 population
  5. Stroke: 38.8 deaths per 100,000 population

Life expectancy saw a decline, with males seeing a drop from 76.3 in 2019 to 74.2 in 2020, and females from 81.4 to 79.9. Notably, females consistently exhibited higher life expectancy rates than males.

In advanced age, different statistics apply. For 2020, life expectancy at 65 for the overall population was 18.5 years—down from 19.6 in 2019. Specifically, for males, it declined from 18.2 in 2019 to 17.0 in 2020, and for females, it went down from 20.8 in 2019 to 19.8 in 2020.

Related Terms: life expectancy, death benefit claims, pension plan, actuary, mortality tables.

References

  1. Centers for Disease Control and Prevention. “Mortality in the United States, 2020”, Page 1.
  2. Centers for Disease Control and Prevention. “Mortality in the United States, 2020”, Page 4.

Get ready to put your knowledge to the test with this intriguing quiz!

--- primaryColor: 'rgb(121, 82, 179)' secondaryColor: '#DDDDDD' textColor: black shuffle_questions: true --- ## What is meant by the term "underlying mortality assumption" in financial terms? - [ ] Estimations based on historical stock prices - [x] Assumptions about the probability of death within a specific population - [ ] Predictions related to market volatility - [ ] Calculations related to pension fund growth ## In which area of finance is the underlying mortality assumption most commonly used? - [ ] Equity trading - [ ] Forex trading - [x] Life insurance and pensions - [ ] Cryptocurrency investments ## Why are underlying mortality assumptions critical for life insurance companies? - [ ] To determine company dividend payouts - [x] To properly price life insurance policies and annuities - [ ] To forecast future market trends - [ ] To establish credit ratings ## How can inaccuracies in underlying mortality assumptions impact an insurance company? - [ ] Increase in policyholder complaints - [ ] Greater customer loyalty - [x] Financial losses or profitability issues - [ ] Improved market predictions ## What data is typically used to create underlying mortality assumptions? - [ ] Stock market indices - [ ] Commodity prices - [x] Demographic statistics and historical mortality rates - [ ] Foreign exchange rates ## Which organization often provides mortality tables that help in setting underlying mortality assumptions? - [ ] New York Stock Exchange (NYSE) - [x] Society of Actuaries (SOA) - [ ] International Monetary Fund (IMF) - [ ] World Bank ## In what way do underlying mortality assumptions affect pension plans? - [x] They are used to calculate the expected payout obligations - [ ] They determine the annual contribution limits - [ ] They help predict future market returns - [ ] They are used to estimate transaction fees ## Different mortality assumptions can lead to different outcomes. What is one major implication of underestimating mortality rates? - [ ] Overestimating investment returns - [ ] Underreporting tax obligations - [x] Underfunded insurance liabilities - [ ] Surplus in unused reserves ## How can advances in healthcare and medicine affect underlying mortality assumptions? - [ ] They can reduce the accuracy of stock market forecasts - [x] They can lead to updated, lower mortality rates - [ ] They can stabilize currency fluctuations - [ ] They can increase commodity prices ## Which financial product is directly influenced by underlying mortality assumptions besides insurance policies? - [ ] Savings accounts - [x] Annuities - [ ] Mutual funds - [ ] Corporate bonds