What Is the Chain Ladder Method?
The Chain Ladder Method (CLM) is a method for computing the claims reserve requirement in an insurance company’s financial statement. The chain ladder method is used by insurers to forecast the amount of reserves that must be established in order to cover projected future claims by projecting past claims experience into the future. CLM therefore only works when prior patterns of losses are assumed to persist in the future. When insurer’s current claims experience changes for some reason, the chain-ladder method will not produce an accurate estimate without proper adjustments.
This actuarial method is one of the most popular reserve methods used by insurance companies. The chain ladder method can be compared with the Bornhuetter-Ferguson Technique and Expected Loss Ratio (ELR) method for calculating insurance company reserves.
- The chain ladder method (CLM) is a popular way that insurance companies estimate their required claim reserves.
- CLM computes incurred but not reported (IBNR) losses by way of run-off triangles, a probabilistic binomial tree that contains losses for the current year as well as premiums and prior loss estimators.
- The underlying assumption of the chain ladder method is that past claims experience is a good predictor of future outcomes.
Chain Ladder Method
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. 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 forecasted, along with the amount of claims that are actually paid, determine how much profit the insurer will publish in its financial documents.
Run-off triangles (or delay 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 its core, 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.
Steps for Applying Chain Ladder Method
According to Jacqueline Friedland’s “Estimating Unpaid Claims Using Basic Techniques,” the seven steps to applying the chain-ladder method are:
- Compile claims data in a development triangle
- Calculate age-to-age factors
- Calculate averages of the age-to-age factors
- Select claim development factors
- Select tail factor
- Calculate cumulative claim development factors
- Project ultimate claims
Age-to-age factors, also called loss development factors (LDFs) or link ratios, represent the ratio of loss amounts from one valuation date to another, and they are intended to capture growth patterns of losses over time. These factors are used to project where ultimate amount of losses will settle.