Friedland15.Evaluation

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

Chapter 15: Evaluation of Methods

Study Tips

This is a really great chapter because it ties together concepts from all previous chapters. The only thing that's totally new is a formula for calculating expected emergence. There are 2 versions and they are both pretty easy:

• expected emergence of reported losses
• expected emergence of paid losses

Most of the exam questions from this chapter ask you to do one or more of the following:

• evaluate the accuracy of an estimate from a given reserving method
• identify a scenario that explains changes in estimates for a given reserving method between successive evaluation dates
• identify a scenario that explains differences in estimates between 2 different reserving methods
• suggest an adjustment or alternate method to improve accuracy of estimates

The best way to get the hang of answering these types of questions is to go through all old exam problems. There are a lot but don't let that scare you – many of them don't take long to do. It's a heavily tested chapter and is an excellent way to review concepts related the reserving methods from previous chapters.

Estimated study time: several days (not including subsequent review time)

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.Fall #25)	emergence: - of losses	unpaid estimate: - recommendation
E (2018.Spring #24)
E (2018.Spring #26)
E (2017.Fall #27)	assess method: - paid & rptd devlpt	assess method: - Freq-Sev, rptd BF	assess method: - paid devlpt, paid BF
E (2017.Fall #28)	assess CDFs: - devlpt, BF, Freq-Sev	assess ECR: - BF method
E (2017.Spring #21)	identify scenario: - consistent with results	identify scenario: - explains difference	alternate method: - for part (a)	alternate method: - for part (b)
E (2017.Spring #26)	assess ECR: - BF method	method comparison: to Benktander	investigating results: - questions for mgmt 1
E (2016.Fall #16)	Friedland05.Triangles	Friedland05.Triangles	Friedland06.Diagnostics	select method: - settlement rate change
E (2016.Fall #18)	BF ECR: - calculate	unpaid: - 2 methods & justify	assess methods: - ECR, paid BF, paid devlpt
E (2016.Fall #27)	actual vs expected: - claim count emergence	actual vs expected: - identify limitation
E (2016.Spring #24)	actual vs expected: - rptd claim emergence	actual vs expected: - actuary's reactions
E (2016.Spring #25)	identify scenario: - explains difference	identify scenario: - explains difference
E (2015.Fall #19)	impact/solution: - long development pattern	impact/solution: - tort reform	impact/solution: - higher deductibles	impact/solution: - faster claims processing
E (2015.Fall #22)
E (2015.Fall #25)
E (2014.Fall #19)
E (2014.Fall #22)
E (2014.Fall #24)
E (2014.Spring #17)
E (2014.Spring #22)
E (2013.Fall #18)
E (2013.Fall #24)
E (2013.Spring #21)
E (2013.Spring #22)
E (2013.Spring #26)

¹ See Friedland04.Meetings for potential questions.

In Plain English!

Selecting a Final Estimate

Suppose you get to the end of your reserve analysis and have estimates of ultimate from several different methods:

• insert example

use example to discuss concepts from chapter

Emergence Patterns

• formula...
• limitations of using emergence... see 2016.Fall #27

• non-linear emergence (linear interpolation of annual pattern to quarterly may not be accurate but quarterly to monthly may be ok solution use shorter time frames)
• if ultimate is not based on a development method then the CDFs from a development may not appropriate (use paid or reported divided ultimate to get pattern instead of CDFs)
• doesn't account for operational changes (Exs: change in mix of business, settlement rate, case strength) since projected emergence is based on historical patterns solution investigate operation changes then either select data that isn't affected or make adjustments to affected data to correct for distortions caused by operational changes