Friedland07.Development

Reading: Friedland, J.F., Estimating Unpaid Claims Using Basic Techniques, Casualty Actuarial Society, Third Version, July 2010. The Appendices are excluded.

Chapter 7: Development Technique(or Chain Ladder Technique)

Study Tips

This is a long chapter, almost 60 pages, but the development method, also called the chain-ladder method, is not hard. Once you practice it a few times, you'll get the hang of it. It might already be familiar to you anyway. The source text is organized like this:

first 15 pages: (page 84-98) mechanics of the development method

• this the single most important topic on the Exam 5 syllabus
• source text explains the method in words but it's simpler just to look at examples (provided in this wiki article)

next 8 pages: (page 98-105) influences of a changing environment

• You need to understand how changes in the environment affect the accuracy of the development method (this is important)
• I have used my own policy & claims simulation software called SimPolicy to create illustrative examples
• video explanations are provided for selected examples

last 25 pages: (page 106-130) PDFs of Excel spreadsheet to illustrate the material

• these examples in the source text are very detailed (maybe a little too detailed)
• you can take a look at these if you'd like but make sure you look at my examples first

Note: If you're reading this chapter as part of your first pass through the pricing material, you only need to know the first 15 pages on the mechanics of the method. If you're studying the reserving material, then you should study the whole chapter.

Estimated study time: 2-5 days depending on whether you're covering this material for pricing or reserving (not including subsequent review)

BattleTable

Based on past exams, the main things you need to know (in rough order of importance) are:

• fact A...
• fact B...

reference	part (a)	part (b)	part (c)	part (d)
E (2019.Spring #16)	ultimate: - rptd devlpt	industry data: - considerations	impact: - of operational change
E (2019.Spring #19)	ultimate: - rptd devlpt (tort reform)	Friedland09.BornFerg
E (2018.Spring #20)
E (2017.Fall #17)
E (2017.Fall #19)	ultimate: - rptd devlpt	diagnostic: - operational changes	accuracy issues: - unpaid claim data
E (2017.Spring #20)
E (2016.Fall #25)
E (2016.Spring #14)
E (2016.Spring #16)	Friedland08.ExpectedClms	ultimate: - paid devlpt	Friedland09.BornFerg
E (2015.Fall #22)
E (2015.Spring #23)
E (2014.Spring #13)
E (2013.Spring #16)

In Plain English!

Before getting to the first example Alice wanted me to mention the different terms for a very important concept regarding the development method. These terms all mean the same thing:

• age-to-age factor
• development factor
• Loss Development Factor
• link ratio

Friedland uses the term age-to-age factor but the term Loss Development Factor is probably more common. Plus, it has a nice abbreviation: LDF.

Development Method - Simple Example

You will never have a problem this simple on the exam or in real life. It's purely to teach you the mechanics of the method. Enjoy it before we get to the hard stuff!

Example: calculate the ultimate loss for each AY using the given data

Cumulative paid loss triangle:

AY	12	24	36	48
2020	48	140	201	240
2021	48	140	201
2022	48	140
2023	48

Method: paid loss development

→ Use historical patterns to predict future losses.

→ The text breaks this method into 7 steps but Alice likes to condense it into just 4 steps as follows:

Step A: calculate age-to-age factors or LDFs

• This is incredibly simple:
  • The 1^st age-to-age factor (or LDF) for AY 2020 = 140/48 = 2.92. (You just divide the 24-month value by the 12-month value.)
  • The 2^nd age-to-age factor for AY 2020 = 201/140 = 1.43 (Divide the 36-month value by the 24-month value.)
  • The 3^rd age-to-age factor for AY 2020 = 240/201 = 1.19 (Divide the 36-month value by the 24-month value.)

AY	12-24	24-36	36-48
2020	2.92	1.44	1.19
2021	2.92	1.44
2022	2.92

• Now do the same thing for AY 2021 and AY 2022. For this very simple example the age-to-age factors are the same for each AY. We'll build to more complicated examples once we've covered the basic method. (There is no row for AY 2023 in the LDF triangle because AY 2023 has only 1 data point at age = 12. You would have to wait another year to get the next data point at age = 24 to be able to calculate the 12-24 LDF for AY 2023.)
• Note how the column labels changed. Instead of 12 for the first column, we now use 12-24. That's because the value of 2.92 represents how the value at 12 months ($48) develops to get the value at 24 months ($140).

Step B: select an age-to-age factor (or LDF) for each column

• This is potentially the most complex step because it requires actuarial judgment. The idea is to scan the column from top to bottom and select a representative value that according to Friedland:

...represents the growth anticipated in the subsequent development interval

• It's like an IQ-test! (Pick the next number in the pattern.) And actuaries tend to be very sensitive about selecting LDFs (age-to-age factors). But for certain high-volume line, short-tail lines of business like auto insurance, the computer can make pretty good selections for most development periods. That means the actuary can spend more time on the parts of the analysis that really do require human insight. Click for a Funny Story About Selecting LDFs.

• Anyway, selecting the LDFs is very simple for this example because every value in each column is the same. (Remember, we will build to more complex examples once you understand the basic method.) Our selected LDFs are as follows: (Even Ian-the-Intern could have figured this out!)

	12-24	24-36	36-48	48-ultimate*
selected	2.92	1.44	1.19	1.00   (tail factor)

^* Note the additional column for the tail factor LDF. We'll return to this later but for now just assume there is no development on claims past 48 months. In other words, every development period past 48 months (60, 72,...) will have paid loss equal to the 48-month value of 240. If you were then to calculate the LDFs for 48-60, 60-72,...etc, they would all equal 1.00.

Step C: calculate age-to-ultimate LDFs

• This step does not require judgment. It is just arithmetic.
• In Step B, we calculated age-to-age development factors. Now we multiply them together to get age-to-ultimate development factors. Here's the result. (Explanation below.)

	12-ult	24-ult	36-ult	48-ultimate
selected	5.00	1.71	1.19	1.00   (tail factor)

• To get these values, you have to work backwards, from right to left:

48-ult: No calculation required in this example. Just copy the 1.00 tail factor from Step B.

36-ult: (selected age-to-age LDF) x (prior [age-ult])

= 1.19 x 1.00

= 1.19

24-ult: (selected age-to-age LDF) x (prior [age-ult])

= 1.19 x 1.19

= 1.71

12-ult: (selected age-to-age LDF) x (prior [age-ult])

= 2.92 x 1.71

= 5.00

• When you reproduce these calculations, you may see minor difference due to rounding because I'm only showing 2 decimal places. I didn't want to clutter the presentation with a bunch of extra decimal places.

Step D: calculate ultimate losses based on the latest diagonal of paid losses (these are the values in brown font from the original triangle)

• Here's the result. I've reproduced the latest diagonal from the original paid loss triangle just for convenience but you don't technically have to do that.

	AY 2023	AY 2020	AY 2021	AY 2020
diagonal	48	140	201	240
ultimate	240	240	240	240

AY 2020: diagonal x (age-ult)

= 240 x 1.00

= 240

AY 2021: diagonal x (age-ult)

= 201 x 1.19

= 240

AY 2021: diagonal x (age-ult)

= 140 x 1.71

= 240

AY 2021: diagonal x (age-ult)

= 48 x 5.0

= 240

• Again, please ignore the minor rounding differences. Take a quick look at the following link, which lays out the solution to the above problem concisely. There are also a few extra comments included within the solution that might be worth looking at.

Demo F-07: Development Method - Simple Example

Development Method - A Few More Examples

Notice that Steps A, C, D in the development method were formula-based calculations. No actuarial judgment required. But Step B was different. The actuary had to select LDFs for each column that "fit the pattern" from previous years. And your final estimates of AY ultimates can vary greatly depending on what you select. In the simple example from above, even Ian-the-Intern could select good LDFs. That's because the development pattern was consistent from year to year. In other words, the way losses (or claims) developed from one period to the next was the same for every row or AY. Mathematically, that means the calculated age-to-age development factors were the same within each column.

Key Assumption: The development method assumes future loss development is similar to development in prior years

Pop Quiz A!    :-o

• Identify which of these paid loss triangles satisfy the key assumption of the development. Click for Answer 

(You have to ask yourself whether the development from year to year is similar enough. It's never going to be exact.)

Triangle 1	12	24	36	48
2020	48.1	141.2	200.7	240.0
2021	47.4	140.5	201.0
2022	48.2	139.6
2023	48.0

Triangle 2	12	24	36	48
2020	48.0	140.4	200.7	240.0
2021	43.6	136.0	198.6
2022	40.0	132.1
2023	39.6

Triangle 3	12	24	36	48
2020	42.4	145.7	202.3	240.0
2021	56.1	144.0	205.3
2022	52.1	137.2
2023	42.2

Triangle 4	12	24	36	48
2020	46.8	144.3	196.9	240.0
2021	53.3	145.4	203.1
2022	60.0	151.2
2023	68.6

Demo F-07: Development Method - Full Solutions to Pop Quiz A

Pop Quiz A - Answer

To answer this question you have to do Step A of the development method where you calculate the LDFs (or link ratios or age-to-age factors or whatever you like to call them.)

• Triangle 1: satisfies stability/consistency assumption for loss development to work reasonably well
• Triangle 2: doesn't satisfy stability/consistency assumption (LDFs are increasing within columns)
• Triangle 3: hard to tell - the underlying development pattern is the same for each AY but the simulation introduced moderate random variation
• Triangle 4: doesn't satisfy stability/consistency assumption (LDFs are decreasing within columns)

Triangle 1 LDFs	12-24	24-36	36-48
2020	2.936	1.421	1.196
2021	2.963	1.430
2022	2.897

Triangle 2 LDFs	12-24	24-36	36-48
2020	2.924	1.430	1.196
2021	3.116	1.461
2022	3.302

Triangle 3 LDFs	12-24	24-36	36-48
2020	3.436	1.388	1.186
2021	2.568	1.426
2022	2.636

Triangle 4 LDFs	12-24	24-36	36-48
2020	3.085	1.364	1.219
2021	2.726	1.397
2022	2.520

Go back