Expected loss ratio (ELR method) is a technique used to determine the projected amount of claims, relative to earned premiums. The expected loss ratio (ELR) method is used when an insurer lacks the appropriate past claims occurrence data to provide because of changes to its product offerings and when it lacks a large enough sample of data for long-tail product lines. It is used to calculate the Ultimate Claims further it calculates the IBNR.
The Formula for the ELR Method Is
Ultimate claims using Expected Loss Ratio Method (ELR Method) =EarnedPremiums ∗ ExpectedLossRatio
How to Calculate Ultimate Claime using Expected Loss Ratio – ELR Method
What Does the ELR Method Tell You?
Insurers set aside a portion of their premiums from underwriting new policies in order to pay for future claims. The expected loss ratio is used to determine how much they set aside. It’s also important to note that the frequency and Severity refers to the amount you have received Insurance claim for. Average Severity would be the loss associated with an average Insurance claim. of the claims they expect to experience also plays a role. Insurers use a variety of forecasting methods in order to determine claims reserves.
In certain instances, such as new lines of business, the ELR method may be the only possible way to figure out the appropriate level of loss reserves required. The ELR method can also be used to set the loss reserve for particular business lines and policy periods. The expected loss ratio, multiplied by the appropriate An earned premium is the premium collected by an insurance company for the portion of a policy that has expired. In other words, the earned premium is what the insured party has paid for a portion of time in which the insurance policy was in effect, but has since expired. figure, will produce the estimated ultimate losses (paid or incurred). However, for certain lines of business, government regulations may dictate the minimum levels of loss reserves required.
- Used to determine the projected amount of claims, relative to An earned premium is the premium collected by an insurance company for the portion of a policy that has expired. In other words, the earned premium is what the insured party has paid for a portion of time in which the insurance policy was in effect, but has since expired..
- Insurers set aside a portion of premiums from policies to pay for future claims—the expected loss ratio determines how much they set aside.
- ELR is used for businesses or business lines that lack past data, while the chain ladder method is used for stable businesses.
Example of How to Use Expected Loss Ratio (ELR) Method
Insurers use expected loss ratio to calculate the incurred but not reported (IBNR)reserve and total reserve. The expected loss ratio is the ratio of ultimate losses to earned premiums. The ultimate losses can be calculated as the earned premium multiplied by the expected loss ratio. The total IBNR reserve is calculated as the ultimate losses less Incurred Losses (paid and outstanding losses).
For example, an insurer has earned premiums of INR 10,000,000 and an expected loss ratio of 0.60. Over the course of the year, it has reported losses of INR 750,000. The insurer’s total IBNR reserve would be INR 5,250,000 (INR 10,000,000 * 0.60 – INR 750,000),
The Difference Between the ELR Method and the Chain Ladder Method (CLM)
Both the ELR and the chain ladder method(CLM) measure claim reserves, where the CLM uses past data to predict what happens in the future. While the expected loss ratio (ELR) is used when there’s little past data to go off of, CLM is used for stable businesses and business lines where the development takes place over long term and is more than 60% – 70%.
Limitations of Using the ELR Method
The amount of claims reserves that an insurer should set aside is determined by actuarial models and forecasting methods. Insurers often use the expected loss ratio on the amount and quality of data that is available. It is often useful in the early stages of forecasting because it does not take into account actual paid losses, but in later stages, this lack of sensitivity to changes in reported and paid losses makes it less accurate and thus, less useful.
Learn Excel or Python/R for Data Science
If you want to learn Python and Machine learning, the course – Machine Learning A-Z™: Hands-On Python & R In Data Science is the best to compete in the market. For Excel, refer Microsoft Excel – Excel from Beginner to Advanced. The course teaches you whatever you are looking to learn in Excel.