Friedland03.Data

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

Chapter 3: Understanding the Types of Data Used in the Estimation of Unpaid Claims

Study Tips

There is a lot of good, basic information in this chapter but aside from a few key facts to memorize, it isn't something you can fully absorb on your first pass. You are probably familiar with some of it anyway from your work duties. So don't spend too long here – you can always refer back when necessary. Among the facts you have to memorize are:

• advantages/disadvantages of various data aggregation methods (CY, AY, RY, PY)

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 #13)	combining data: - argue for	combining data: - argue against	rate recommendation: - provide comment
E (2017.Spring #14)	wrong chapter - move to 7
E (2016.Fall #17)	data for analysis: - compare strategies
E (2016.Spring #15)	Friedland05.Triangles	data aggregation: - is CY appropriate?	data aggregation: - is AY appropriate?	Friedland07.Development
E (2015.Fall #14)	Friedland07.Development	Friedland07.Development
E (2015.Fall #15)	large claim thresold: - selection considerations	Friedland09.BornFerg
E (2015.Fall #16)	Friedland05.Triangles	data aggregation: - RY versus AY

In Plain English!

Organizing the Data

We'll start with some very basic information. This would likely never be asked on the exam but it's something you should know.

Question: what are the 5 key dates for the organization of claim data [Hint: PARAV]

Policy effective dates: beginning and ending dates of the policy term

Accident date: when the accident or event occurred that triggered coverage

Report date: when the claim was reported/recorded in the claim system

Accounting date: defines a group of claims for which liability exists, often Dec 31 of the given year

Valuation date : the date through which transactions are included in the data used by the actuary for the analysis, often Dec 31 of the given year

• The first 3 term above are completely obvious. The accounting date is often referred to as when the books close, either for the month, quarter, of year. The accounting date and valuation date are usually the same. If the books close on Dec 31, the actuary would normally do the reserve analysis using data through Dec 31 (valuation date of Dec 31).

The rest of this section is stuff you definitely have to know as it does come up on the exam.

Question: identify 4 common ways of aggregating data

• CY, AY, PY, RY

→ The terms are used very often and I don't want to write them out in full every time. They are, respectively, Calendar Year, Accident Year, Policy Year, Report Year. Note that Policy Year is also sometimes called Underwriting Year.

define & describe: CY aggregation of data

definition: CY data includes all data with a transaction date within the given year.

use:

- premium and exposure data is usually aggregated by CY

- loss data is not usually aggregated by CY, except possibly for diagnostics

- here's a very useful formula for CYEP, Calendar Year Earned Premium,

CYEP   =   WP + ( UEPbeg – UEPend )

advantages:

- readily available

- no future development (data doesn't change after accounting date)

disadvantages:

- no future development (generally cannot use it for a reserve analysis)

define & describe: AY aggregation of data

definition: AY data includes all data with an occurrence date within the given year.

use:

- loss data is very commonly aggregated by AY

advantages:

- AY loss aggregation is the accepted norm in the U.S. and Canada (so their is uniformity)

- easy to obtain & understand

- industry benchmarks are available

disadvantages:

- mismatch between AY losses and CY premiums or exposures (premiums & exposures are usually aggregated by CY)

- AY losses may contain policies written at different price levels (because may contain policies from different PYs)

- AY losses may contain policies written at different retention levels mask changes in retention level (because may contain policies from different PYs)

define & describe: PY aggregation of data

definition: PY data includes all data with an policy effective date within the given year.

use:

- great for self-insurers because they have only 1 policy (their own)

advantages:

- perfect match between losses and premiums or exposures

- easier to isolate effects of policy changes from year to year (because all policies in a PY will have the same policy characteristics)

disadvantages:

- extend time frame of 24 months (an accident may occur on day 1 for a policy written on Jan 1, all the way through to day 365 for a policy written on Dec 31)

- harder to isolate effects of catastrophes or court rulings that happen on a specific calendar date (because policies from the previous and current PYs would be affected)

define & describe: RY aggregation of data

definition: RY data includes all data with a report date within a given year.

use:

- great of claims-made policies like medical malpractice or product liability (policies where coverage is triggered by the reporting of a claim versus the date of the event)

advantages:

- number if claims is fixed at the close of a RY (unlike an AY where the number of claims can still increase due to late-reported claims)

- development patterns are more stable (because number of claims is fixed, and there is only IBNER, no pure IBNR)

disadvantages:

- only measures development on known claims (unreported claims get dumped into the subsequent RY so there's a potential lag in understanding the true extent of an insurer's liabilities)