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CREDIBILITY

FROM THE EYES OF THE CUSTOMER









Michael Round

Rational Systems



August 11, 1999

Rational Systems, formed by Michael Round in 1999, and

located in Overland Park, Kansas, specializes in statistical and

actuarial improvement.

For a copy of “Credibility”, please e-mail me at:

roundml@ix.netcom.com





We are now accepting clients.

INTRODUCTION &

BACKGROUND

BACKGROUND





DISRAELI vs. HUCKLEBERRY FINN

INTRODUCTION &

BACKGROUND

The Quality

Presentation of Subject

Well Disraeli Transaction

Communication /







= win/win







Poorly Hack Huck





Poor Idea Good Idea







Appropriateness / Relevance of

Idea

THE EXISTENCE OF A

PROBLEM

CONTEXT: the renewal rating process, the pricing actuary, and

the group representative / layperson - all addressing the impending

anniversary date of the group.

THE ACTUARIAL POSITION

goal requirements prerequisite

The pricing Credibility is used

actuary must take in the renewal

into account claims rating process.

variability.

We want to have a

reasonably priced

insurance product.

THE GROUPS’ POSITION

goal requirements prerequisite









We want to have a

reasonably priced

insurance product.





My premium Numbers “speak

should reflect my for themselves”...

experience. no need for

„credibility‟.

THE CONFLICT

goal requirements prerequisite

The pricing Credibility is used

actuary must take in the renewal

into account claims rating process.

variability.

We want to have a

reasonably priced

insurance product.





My premium Numbers “speak

should reflect my for themselves”...

experience. no need for

„credibility‟.

CONFLICT CONSEQUENCES



Credibility may My expectation is a

adjust data to an low renewal rate

“average renewal increase.

rate increase”.









Credibility is used My group has an Numbers “speak

in the renewal empirically low loss for themselves”...

rating process. ratio. no need for

„credibility‟.

UNDESIREABLE EFFECT



Unhappy, angry,

and confused, I go

to the market for

competing quotes.









Credibility may My expectation is a

adjust data to an low renewal rate

“average renewal increase.

rate increase”.

THE ROOT CAUSE



At first glance, we might suggest the root cause of the conflict and the

inevitable action of the group is:





FAILURE OF THE CLIENT TO UNDERSTAND THAT

ADJUSTMENT OF DATA IS NECESSARY IN THE RENEWAL

PROCESS . . . After all, the client has the perception:





THE DATA IS WHAT IT IS!!



However ...

DATA ADJUSTMENTS THAT

ARE UNDERSTOOD

We often adjust the data prior to the application of credibility, and the

group / layperson perfectly understands “the data is NOT what it is” -

and is comfortable with data adjustments.







For example …





First-Year Cases on a Paid Basis

Late-Year Rapid Contract Growth

DATA ADJUSTMENTS THAT

ARE UNDERSTOOD

Empirical Data Apples-to-Apples

Paid Paid Paid Paid

Month Premium Claims Premium Claims

January $10,000 $0 $0 $0

February $10,000 $0 $0 $0

March $10,000 $0 $0 $0

April $10,000 $7,143 $10,000 $7,143

May $10,000 $10,000 $10,000 $10,000

June $10,000 $14,286 $10,000 $14,286

July $10,000 $5,714 $10,000 $5,714

August $10,000 $2,857 $10,000 $2,857

September $10,000 $3,571 $10,000 $3,571

October $10,000 $5,143 $10,000 $5,143



Totals $100,000 $48,714 $70,000 $48,714



Loss Ratio 49% 70%

DATA ADJUSTMENTS THAT

ARE UNDERSTOOD

Empirical Data Apples-to-Apples

Paid Paid Paid Paid

Month Contracts Premium Claims Premium Claims

January 100 $10,000 $7,143 $10,000 $7,143

February 100 $10,000 $10,000 $10,000 $10,000

March 100 $10,000 $14,286 $10,000 $14,286

April 100 $10,000 $5,714 $10,000 $5,714

May 100 $10,000 $2,857 $10,000 $2,857

June 100 $10,000 $3,571 $10,000 $3,571

July 100 $10,000 $5,143 $10,000 $5,143

August 200 $20,000 $6,959 $10,000 $6,959

September 200 $20,000 $6,933 $10,000 $6,933

October 200 $20,000 $6,495 $10,000 $6,495



Totals $130,000 $69,101 $100,000 $69,101



Loss Ratio 53% 69%

THE ROOT PROBLEM



The group often does not

understand the adjustment

of their “good” data when

adjusted via credibility.









The group accepts “data Credibility adjusts data in a

adjustments” they can numerical “black box”.

understand.

INJECTION TO SOLVE THE

PROBLEM

The group has an understanding of what has

happened to their data - and may not be as

dissatisfied with the perceived gap between the

“expected” and “actual” renewal rate increase.









The group accepts “data CREDIBILITY - and what

adjustments” they can gives rise to its need - ARE

understand. INTUITIVELY presented.

THE SOLUTION TO THE

PROBLEM

CREDIBILITY seeks to quantify the variability, uncertainty,

and predictability of a block of data.

To intuitively present this idea, we need to bridge the gap

between our level of knowledge and that of the group

representative / lay-person - OR PROVIDE A VISUAL

MEANS OF AFFORDING THE LAY-PERSON THE

INTUITIVE KNOWLEDGE OF WHAT VARIABILITY IS.









SIMULATION is an excellent mechanism for

demonstrating variability and uncertainty at an intuitive

level.

SIMULATING VARIABILITY

IF the essence of credibility is “degree of predictability”,

THEN one method of communicating intuitively this concept

is a simple simulation probability distribution for three

different group sizes:





25 Members

250 Members

2500 Members

A ‘25-MEMBER’ GROUP ...

A ‘250-MEMBER’ GROUP ...

A ‘2500-MEMBER’ GROUP ...

CLAIMS: A COMPARISON BY

GROUP SIZE

EXPLAINING CREDIBILITY

IF we have achieved our goal of visually demonstrating the

meaning of “variability”, “uncertainty”, and “predictability”

[all of which CREDIBILITY is addressing], THEN the group

representative / lay-person has some feel why their data is being

adjusted.









WHY NOT USE THE SAME SIMULATION PROCESS THAT

EXPRESSES VARIABILITY VISUALLY TO TABULATE

THE CREDIBILITY FACTORS WE NEED TO ACTUALLY

ADJUST THE DATA?

THE ADVANTAGES

When SIMULATION is used as described here, the advantages

are numerous:





1. The source data [the continuance table] is ours;

2. We can visually and easily explain what the whole process

means;

3. We can quantify „credibility‟ numerous ways;

4. We can easily make continuance table adjustments to

recognize certain characteristics of a group [e.g., low

specific deductibles, carve-outs, annual maximums];

5. We can operationally define to all parties exactly what is

going on.

TABULATING CREDIBILITY

There are many ways to tabulate credibility when approached

from the perspective defined here. The two basic

categories are:





1. Percentage of simulations falling within +/- x% of the

expected PMPM;

2. Percentage of simulation dollars falling within +/- x% of the

total expected dollars.

CREDIBILITY FOR OUR

PURPOSES

CREDI BI LI TY TABU LAR DATA CREDI BI LI TY TABU LAR DATA

PROBABI LI T Y M EASU RE: # OF SI M U LAT ED GROU PS PROBABI LI T Y M EASU RE: AM OU N T OF SI M U LAT ED CLAI M $





Simulated Rates Group Size in Members Simulated Rates Group Size in Members

in Relation to in Relation to

Expected Rates 10 25 50 100 250 500 1000 2500 5000 Expected Rates 10 25 50 100 250 500 1000 2500 5000

0%-5% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0%-5% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

5%-15% 3.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 5%-15% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

15%-25% 8.6% 1.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 15%-25% 1.8% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

25%-35% 11.2% 5.9% 1.6% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 25%-35% 3.4% 1.8% 0.5% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

35%-45% 10.6% 9.3% 5.5% 1.7% 0.0% 0.0% 0.0% 0.0% 0.0% 35%-45% 4.3% 3.8% 2.3% 0.7% 0.0% 0.0% 0.0% 0.0% 0.0%

45%-55% 10.6% 12.3% 11.1% 6.4% 0.8% 0.1% 0.0% 0.0% 0.0% 45%-55% 5.3% 6.3% 5.6% 3.2% 0.4% 0.0% 0.0% 0.0% 0.0%

55%-65% 8.7% 12.5% 14.3% 13.6% 6.9% 1.7% 0.1% 0.0% 0.0% 55%-65% 5.2% 7.7% 8.7% 8.2% 4.3% 1.1% 0.1% 0.0% 0.0%

65%-75% 7.9% 11.1% 14.1% 16.3% 15.7% 9.2% 3.9% 0.3% 0.0% 65%-75% 5.5% 7.9% 10.0% 11.4% 11.1% 6.5% 2.8% 0.2% 0.0%

75%-85% 6.9% 9.1% 11.7% 14.5% 18.3% 20.3% 17.5% 7.7% 2.3% 75%-85% 5.6% 7.4% 9.4% 11.5% 14.8% 16.2% 14.1% 6.3% 1.9%

85%-95% 5.0% 7.8% 9.6% 11.1% 15.8% 20.9% 25.1% 29.4% 27.6% 85%-95% 4.6% 7.2% 8.7% 10.0% 14.3% 18.7% 22.6% 26.7% 25.2%

95%-105% 4.0% 5.3% 6.3% 7.9% 13.1% 15.4% 20.9% 33.2% 44.3% 95%-105% 4.1% 5.4% 6.3% 7.9% 13.1% 15.3% 20.9% 33.2% 44.3%

105%-115% 3.0% 4.2% 4.8% 6.3% 8.1% 10.3% 12.8% 19.3% 21.1% 105%-115% 3.4% 4.8% 5.3% 6.9% 9.0% 11.3% 14.0% 21.1% 23.0%

115%-125% 2.5% 3.5% 3.5% 4.1% 5.1% 6.7% 8.9% 7.2% 4.3% 115%-125% 3.1% 4.3% 4.2% 4.9% 6.1% 8.0% 10.7% 8.6% 5.1%

125%-135% 2.6% 2.5% 2.9% 3.5% 3.4% 4.6% 5.5% 2.2% 0.4% 125%-135% 3.4% 3.4% 3.8% 4.6% 4.5% 5.9% 7.1% 2.9% 0.5%

135%-145% 1.9% 1.7% 2.0% 2.9% 2.9% 3.0% 2.5% 0.5% 0.0% 135%-145% 2.7% 2.5% 2.8% 4.1% 4.1% 4.2% 3.5% 0.7% 0.0%

145%-155% 1.6% 1.4% 1.4% 2.1% 2.2% 2.6% 1.4% 0.1% 0.0% 145%-155% 2.4% 2.2% 2.1% 3.2% 3.3% 3.8% 2.1% 0.2% 0.0%

155%-165% 1.0% 1.2% 1.4% 1.9% 1.8% 2.0% 0.9% 0.0% 0.0% 155%-165% 1.7% 2.0% 2.3% 3.0% 3.0% 3.2% 1.4% 0.0% 0.0%

165%-175% 1.1% 1.1% 1.2% 1.1% 1.2% 1.3% 0.3% 0.0% 0.0% 165%-175% 1.8% 1.9% 2.0% 1.9% 2.0% 2.2% 0.5% 0.0% 0.0%

175%-185% 0.9% 0.9% 1.3% 0.7% 0.8% 0.7% 0.1% 0.0% 0.0% 175%-185% 1.7% 1.6% 2.3% 1.2% 1.4% 1.2% 0.2% 0.0% 0.0%

185%-195% 0.9% 0.8% 0.8% 0.4% 0.4% 0.7% 0.1% 0.0% 0.0% 185%-195% 1.8% 1.6% 1.6% 0.8% 0.8% 1.2% 0.1% 0.0% 0.0%

195%-205% 0.5% 0.6% 0.7% 0.3% 0.5% 0.2% 0.0% 0.0% 0.0% 195%-205% 1.0% 1.3% 1.4% 0.7% 0.9% 0.3% 0.0% 0.0% 0.0%

205%-215% 0.4% 0.7% 0.9% 0.2% 0.6% 0.1% 0.0% 0.0% 0.0% 205%-215% 0.8% 1.4% 1.9% 0.4% 1.2% 0.2% 0.0% 0.0% 0.0%

215%-225% 0.3% 0.4% 0.5% 0.3% 0.6% 0.1% 0.0% 0.0% 0.0% 215%-225% 0.7% 0.9% 1.1% 0.6% 1.3% 0.3% 0.0% 0.0% 0.0%

225%-235% 0.2% 0.3% 0.5% 0.4% 0.6% 0.1% 0.0% 0.0% 0.0% 225%-235% 0.6% 0.8% 1.2% 1.0% 1.5% 0.1% 0.0% 0.0% 0.0%

235%-999% 6.3% 5.4% 3.9% 4.2% 1.2% 0.1% 0.0% 0.0% 0.0% 235%-999% 34.8% 23.6% 16.6% 13.7% 3.1% 0.3% 0.0% 0.0% 0.0%



CREDI BI LI TY CREDI BI LI TY

as # of Gr oups as # of Claim $

with exper ience 12.1% 17.3% 20.7% 25.3% 37.0% 46.7% 58.8% 81.9% 93.0% with exper ience 12.0% 17.3% 20.3% 24.8% 36.4% 45.3% 57.4% 81.0% 92.5%

within +/- 15% within +/- 15%

of expected r ates of expected r ates



as # of Gr oups as # of Claim $

with exper ience 21.5% 29.9% 35.8% 43.9% 60.4% 73.7% 85.2% 96.8% 99.6% with exper ience 20.6% 29.0% 33.9% 41.3% 57.2% 69.5% 82.2% 95.9% 99.5%

within +/- 25% within +/- 25%

of expected r ates of expected r ates

CREDIBILITY FOR OUR

PURPOSES

100%

100% 97%

93%

Credibility Defined in Terms of

"Number of Groups" within 85%

82%

Certain Bounds 74%

75%



60% 59%





50% 47%

44%



36% 37%

30%

25%

25% 22% 21%

17%

12%







0%

10 25 50 100 250 500 1000 2500 5000

Group Size: Members

Number of Groups with +/- 15% of Expected Rates Number of Groups within +/- 25% of Expected Rates

CREDIBILITY FOR OUR

PURPOSES

100%

100% 96%

Credibility Defined in Terms of 93%



"Amount of Claim $" within 82% 81%

Certain Bounds

75% 70%





57% 57%





50% 45%

41%

36%

34%

29%

25%

25% 21% 20%

17%

12%







0%

10 25 50 100 250 500 1000 2500 5000

Group Size: Members

Amount of Claim $ with +/- 15% of Expected Rates Amount of Claim $ within +/- 25% of Expected Rates

WHICH METHOD TO USE?

We can obviously choose from a number of

tabular formulations to use as our

“credibility” factors . . . Which one is best?









The control limits of his charts are

Dr. Walter Shewhart, the not solely statistical in nature, but

founder of the Statistical PRACTICAL…that is, Dr.

Process Control Chart, is Shewhart addressed the question

perhaps the best source to “WHAT MAKES ECONOMIC

consult to address this AND PRACTICAL SENSE?” and

question. he developed a process from that

perspective.

THE ANSWER?

What tabular selection is best? Which should be used in

the adjustment of data?





WHICHEVER METHOD IS BEST FOR YOU IN

YOUR MARKET DEALING WITH THE PEOPLE IN

YOUR MARKET AND THE COMPETITION IN YOUR

MARKET.`

CONCLUSION



Have I convinced you there is a problem?



Have I addressed the reason for this problem?





Have I developed the cause of the problem?





Have I solved the problem?





Have I quantified in practical terms what “solution” means?





Have I shown numerous advantages to such a solution, while not

introducing negative consequences?

CONCLUSION

Have I convinced you there is a problem?





Have I addressed the reason for this problem?



Have I developed the cause of the problem?





Have I solved the problem?





Have I quantified in practical terms what “solution” means?





Have I shown numerous advantages to such a solution, while not

introducing negative consequences?

CONCLUSION

Have I convinced you there is a problem?





Have I addressed the reason for this problem?





Have I developed the cause of the problem?



Have I solved the problem?





Have I quantified in practical terms what “solution” means?





Have I shown numerous advantages to such a solution, while not

introducing negative consequences?

CONCLUSION

Have I convinced you there is a problem?





Have I addressed the reason for this problem?





Have I developed the cause of the problem?





Have I solved the problem?



Have I quantified in practical terms what “solution” means?





Have I shown numerous advantages to such a solution, while not

introducing negative consequences?

CONCLUSION

Have I convinced you there is a problem?





Have I addressed the reason for this problem?





Have I developed the cause of the problem?





Have I solved the problem?



Have I quantified in practical terms what

“solution” means?

Have I shown numerous advantages to such a solution, while not

introducing negative consequences?

CONCLUSION

Have I convinced you there is a problem?



Have I addressed the reason for this problem?



Have I developed the cause of the problem?





Have I solved the problem?





Have I quantified in practical terms what “solution” means?



Have I shown numerous advantages to such a

solution, while not introducing negative

consequences?



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