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Term: Chain Ladder Method (CLM)

30 Oct 2015

DEFINITION of ‘Chain Ladder Method (CLM)’
A method for calculating the claims reserve requirement in an insurance company’s financial statement. The chain ladder method (CLM) is used by insurers to forecast the amount of reserves that must be established in order to cover future claims. This actuarial method is one of the most popular reserves methods.

BREAKING DOWN ‘Chain Ladder Method (CLM)’
Insurance companies are required to set aside a portion of the premiums they receive from their underwriting activities to pay for claims that may be filed in the future. The amount of claims that are forecasted, along with the amount of claims that are actually paid, determine how much profit the insurer will publish in its financial documents.

The chain ladder method calculates incurred but not reported (IBNR) loss estimates using run-off triangles of paid losses and incurred losses, representing the sum of paid losses and case reserves. The reserve triangles are two dimensional matrices that are generated by accumulating claim data over a period of time. The claim data is run through a stochastic process to create the run-off matrices after allowing for many degrees of freedom.

At heart, the chain ladder method operates under the assumption that patterns in claims activities in the past will continue to be seen in the future. In order for this assumption to hold, data from past loss experiences must be accurate. Several factors can impact accuracy, including changes to the product offerings, regulatory and legal changes, periods of high severity claims, and changes in the claims settlement process. If the assumptions built into the model differ from observed claims, insurers may have to make adjustments to the model.

Creating estimations can be difficult because random fluctuations in claims data and a small data set can result in forecasting errors. To smooth over these problems, insurers combine both company claims data with data from the industry in general.

Covid-19 – Johns Hopkins University

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