Mini-course 2 of 6

Chain ladder: factors and tail

The chain ladder is the most widely used IBNR method and also the easiest to misuse. This mini-course explains what it assumes, how factors and tails are selected, and how to check that the result makes sense.

30 min
Estimated time
4
Lessons
5
Questions
ObjectivesLessonsWorked exampleSelf-assessmentReadings
01

Objectives

By the end of this mini-course you will be able to:

  • State the chain ladder assumptions and recognize when they fail.
  • Select factors with different averages and justify the choice.
  • Estimate a tail factor and measure its effect on the reserve.
  • Move from factors to ultimates and IBNR, and validate the result with implied loss ratios.
02

Lessons

Lesson 1

What the method assumes

The chain ladder assumes that the future development of each origin year will, on average, be proportional to what has been observed, with a common factor for all years at the same age, and that origin years are independent of one another.

\[\hat C_{i,j+1}=\hat f_j\,C_{i,j},\qquad \hat f_j=\frac{\sum_i C_{i,j+1}}{\sum_i C_{i,j}}\]

Its limits follow from that: it fails when the speed of reporting or payment changes, when case reserving practice changes, or when the latest year has so little information that any deviation is multiplied by a large cumulative factor.

Lesson 2

How to select factors

The volume-weighted average is the method's natural estimator and gives more weight to larger years. The simple average treats every year equally. Averages of the latest three or five years respond to recent trends, and averages that exclude the highest and lowest values reduce the effect of an outlier.

Selecting is not picking the lowest number: it is choosing the average that best describes the future and documenting why. If the diagnostics showed a regime change, a long average mixes two worlds; if they showed a large loss, it is better to exclude it from the factor and reserve it separately.

Lesson 3

The tail factor

When the triangle does not reach full maturity, the last observed factor is not enough: development beyond the last age is missing. That development is the tail factor. It is estimated by fitting a curve to the factors minus one (exponential or inverse power, as Sherman proposed), from industry benchmarks, or from the incurred-to-paid relationship at mature ages.

The tail multiplies every origin year, so a small error in it weighs heavily on the total reserve. That is why it is always worth showing the sensitivity: how much IBNR changes if the tail moves by one point.

Lesson 4

From factor to reserve, and validation

\[CDF_j=f_{\text{tail}}\prod_{k\ge j}\hat f_k,\qquad \hat U_i=C_{i,n+1-i}\,CDF_{n+1-i},\qquad IBNR_i=\hat U_i-C_{i,n+1-i}\]

The inverse of the CDF is the percentage reported at that age. The result is validated in at least three ways: the implied ultimate loss ratio by year should be consistent with pricing and experience; IBNR for the latest year is checked against another method; and at the next valuation, reported development is compared with what the method expected.

03

Worked example

Using the series triangle, we select volume-weighted factors and a 1.01 tail, which we assume is supported by incurred development of older years outside the triangle. Amounts in millions of pesos.

Step 1. Factors and CDFs

Item12-2424-3636-4848-60Tail
Volume-weighted1.84951.19671.06551.03031.0100
Simple average1.84711.19641.06571.0303
Cumulative factor to ultimate (CDF)2.45411.32691.10881.04061.0100
The CDF at each age multiplies the selected factors from that age onward, tail included. For example, the 12-month CDF is 1.8495 × 1.1967 × 1.0655 × 1.0303 × 1.0100 = 2.4541.

Step 2. Ultimates and IBNR

YearIncurredCDF% reportedUltimateIBNRUltimate loss ratio
20212,3801.010099.0%2,403.823.866.8%
20222,5701.040696.1%2,674.4104.470.4%
20232,7801.108890.2%3,082.4302.475.2%
20242,4501.326975.4%3,250.9800.975.6%
20251,4502.454140.7%3,558.42,108.477.4%
Total11,63014,969.93,339.9

Step 3. Reasonableness checks

  1. The simple average gives almost identical factors: total IBNR would be 3,335.2 against 3,339.9. The choice of average does not move the result; the data are consistent in that respect.
  2. Tail sensitivity does matter: with a 1.02 tail instead of 1.01, total IBNR rises by 148.2 million, to 3,488.1.
  3. The implied ultimate loss ratio rises from 66.8% in 2021 to 77.4% in 2025. It may be real, but it matches what we saw in mini-course 1: recent factors are high. 63% of total IBNR sits in 2025, the year with the least information.
Key point: The chain ladder gives IBNR of 3,339.9 million, but almost two thirds of it depends on a single immature year. Before accepting it, compare it with a method that relies less on reported losses, which is the subject of mini-course 3.
04

Self-assessment

Choose one answer per question: you will see right away whether it is correct and why. At the end, check your score. Nothing is stored or sent.

1. Which of these facts most directly violates the chain ladder assumption?
2. What advantage does the volume-weighted average have over the simple average?
3. In the example, the 12-month CDF is 2.4541. What share of 2025 ultimate losses has been reported?
4. Why does an error in the tail factor matter so much?
5. What is the best test that the factor selection was reasonable?
05

Readings

Primary sources and reference texts, all publicly accessible at the time of publication.

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