Understanding Insurance Policies and Risk Management

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  • 0:07 Risk Management and…
  • 1:22 Law of Large Numbers
  • 3:41 Principle of Indemnity
  • 5:11 Subrogation
  • 5:57 Lesson Summary
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Lesson Transcript
Instructor: Shawn Grimsley

Shawn has a masters of public administration, JD, and a BA in political science.

Obtaining insurance is one of the most common methods of risk management. In this lesson, you'll learn about insurance policies and some key concepts related to insurance. You'll also have a chance to take a short quiz.

Risk Management and Insurance Policies

Meet Arnie. He works for an insurance company. Arnie works as an actuary, and his job is very important. He's an expert in higher-level mathematics, probability and statistics. He uses his math skills to help his company assess uncertainty and risk so that the company can decide whether it wants to offer insurance and at what price. Arnie is crucial to the insurance company's risk management, which is the process of identifying, assessing and mitigating risks.

Arnie's work helps an insurance company decide whether to provide insurance to a person or business through issuance of an insurance policy. An insurance policy is a special type of contract that transfers risk from the policyholder to the insurance company in exchange for a fee, called an insurance premium.

Insurance policies often have deductibles, which are out-of-pocket amounts an insured party must pay before the policy kicks in. There are also limits to the amount an insurance company will pay pursuant to the policy. Finally, the policy will have exceptions and exclusions to the coverage.

Law of Large Numbers

Insurance companies are in business to make money and will not assume risk if they believe it is very likely to occur or is inevitable. One of the most important tools in Arnie's arsenal is the law of large numbers. This statistical law states that as the number of exposures increase:

  • The more accurate the prediction of the exposure happening in the future will be
  • There will be less of a deviation from the actual losses incurred and the expected losses
  • The credibility of the prediction increases

Let's use an example to illustrate the point. Let's say that we have samples of 100 men, 1,000 men, 100,000 men and 1,000,000 men who are all aged 65 with the same general risk characteristics, such as non-smokers of normal weight and with no evidence or history of cancer or heart disease.

If you follow these men through one year, you will find that a certain percent dies in each group over the year. The law of large numbers dictates that the percentage of men that die in the 1,000,000-man sample will be more accurate than the 100-man sample.

Why is this important for life insurance companies? Because insurance companies gamble that they will have to pay less in claims than they will bring in through premiums. Arnie makes sure that, through his mathematical prowess, the odds favor the house - or the insurance company. If the odds aren't good, then a policy will be denied or the premium will be very high to compensate for the high-degree of risk.

This also means that the more units you insure, the less important each unit is in calculating the overall exposure to loss. For example, if Arnie's company has a small amount of policies - say 100 - freak occurrences (also known as actuarial abnormalities) can cost the company dearly. On the other hand, if you have a large risk pool, such as 1,000,000 policies, a freak occurrence doesn't affect the overall loss exposure all that much. This is why group insurance is cheaper. The larger the pool of insured people, the lower the risk of the insurance company.

Principle of Indemnity

Meet Carla, Arnie's co-worker. Carla works in the claims department of the insurance company. Her job is to examine claims submitted by insured parties under their insurance policies, determine coverage and pay claims. One of her most important jobs is to determine if the insurance company has to pay a loss, and if so, how much it has to pay under the policy.

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