This document discusses predictive analytics and modeling in life insurance, highlighting the contract between insurers and policyholders, historical context, and benefits of insurance for individuals and companies. It outlines challenges faced by insurance firms, including risk management, premium setting, and underwriting processes, emphasizing the role of predictive modeling in optimizing these aspects. The evolution of predictive analytics in the insurance sector over centuries is also touched upon, showcasing its increasing importance in decision-making and customer assessment.

2. MODULE 1
Predictive Analytics and Modeling in Life Insurance

3. About Life Insurance
• Contract between an insurance policy holder and an insurer or
assurer, where the insurer promises to pay a designated beneficiary
a sum of money (the benefit) in exchange for a premium, upon the
death of an insured person (often the policy holder).
• Two categories: protection and investment policies.
• History: burial clubs in Rome, then in London 1706 there was first
company in London to offer life insurance.

4. • Why is it useful for individuals to have life insurance?
Individual Perspective
o Providing survivors way to pay monthly bills. Also, payment of
descendant's final expenses and mortgages.
o In USA, funding future education expenses.
o Paying amount due for assets.
o Insuring insurability. That is the chance your health gets worse soon.

5. • Insurance companies try to charge a premium and hopefully end
payment will be lower than sum of premiums collected.
• Insurance company takes on risk made by individual. By insuring
large pool of individuals, it diversifies risk and makes it manageable.
• Insurance company has fixed cost (administration and similar) and
also final claim.
• Premiums cover insurance company’s costs and also provide it with
profit.
Benefits for Business – Insurance
Companies

6. • There are legal problems, which we will not discuss in detail in this
course.
• Interesting for us: managing risk. Offering competitive premium
which will still provide insurance company a profit.
• First attempts (still relevant): mortality tables.
• Get the actual duration before person dies as close as possible.
• Special cases: What happens if there is a suicide?
Challenges for Insurance Company
o It gets complicated, but if it is within two years, then, in USA company
does not have to cover. This is different in different countries and even
states.

7. • Defining the best possible premium – amount of money insurance
policy holder pays and how often (usually every month).
• Defining benefits.
• What to do with paid premiums/money? If it just keeps it, inflation
might devalue it.
• Reinsurance problem – what if it comes to shock because of which a
lot of people die at once (example disease)?
• Assurance and insurance: also protection of events that might
happen
Challenges for Insurance Company

8. • Problems are usually of mathematical nature
• Insurance company needs models to reduce risk and predict the
future.
• This helps them with planning and making their business
sustainable.
• There comes the need for predictive models and modeling in
general.
Challenges for Insurance Company

9. Predictive Modeling Usability
• The mortality tables provide a baseline for the cost of insurance, but
the health and family history of the individual applicant is also taken
into account
• The investigation and resulting evaluation is termed underwriting.
• Factors to include:
o Personal medical history
o Family medical history
o Driving record
o Height and weight matrix, BMI (Body Mass Index)

10. Predictive Modeling Usability
• Predictive modeling also improves
marketing and risk classification.
• Fraud detection and differentiation is
another usage.
• Table: potential savings of
representative life insurer by using
predictive modeling.
Source: Batty et al (2010), Predictive modeling for life
insurance, Deloitte consulting LLP

11. Brief History of Predictive Analytics in
Insurance
• 17th century: use of mortality tables (John Graunt and Edmund Halley).
• In 20th century, general insurance actuaries used generalized linear
models and empirical Bayes techniques for pricing of short-term
insurance policies.
• Between 1787 and 1837 more than two dozen life insurance
companies were started, but fewer than half a dozen survived.
• As the time went on, the datasets were becoming bigger and more
things could be used for predictions.

12. Brief History of Predictive Analytics in
Insurance
• The real growth of predictive analytics came in last 50 years.
• They started using questioners and collecting people’s information
about their habits and their past records.
• https://www.geico.com/life-insurance/ - online insurance where
premium is immediately calculated.
• Above uses a lot of different variables.