A City of Two (Re)Tails

2003 Fall CAS Meeting

November 11, 2003

Robert J. Walling

The City of Toningbloom has 2 Hardware Stores…

Soft Hardware Sucha Tools

Different, but eerily the same…

Both are:– Joisted Masonry– Protection Class 6– Same Coverages and Amounts of Insurance

In fact, the properties have identical “traditional” risk characteristics.

They both went insurance shopping…

The quotes came back…

Insurer Soft Quote Sucha Quote

Farm States $1,900 $1,700

Inilli Mutual $1,800 $1,900

OSInsCo $2,100 $2,100

Yankees Ins Co. $1,500 $2,400

Why?

Elementary my Dear Watson…

“Who” Issues, Not “What” Issues

Look at data off the application that is not rated: Percent Occupied Years in Business Years of Same Mgt. Updated Systems Alarms Sole Occupancy Computer Back Ups Hours of Operation Franchise? Safety Program Employee Mix (Full Time, Leased, etc.)

How do companies address these factors?

Traditionally schedule credits and rating tiers?

Company

Tiering

Factor

Schedule

Max/Min

Percent of Manual

Barbershop I.C. 1.25 +40% 175%

Barbershop I.C. 1.25 -40% 75%

Vanilla I.C. 1.00 +40% 140%

Vanilla I.C. 1.00 -40% 60%

BTA I.C. 0.85 +40% 119%

BTA I.C. 0.85 -40% 51%

TPet I.C. 0.70 +40% 98%

TPet I.C. 0.70 -40% 42%

THE HIGHEST

NET RATE IS OVER FOUR TIMES THE

LOWEST!!

Companies are moving to Underwriting Scorecards Using GLM

Applications of GLM for BOP Pricing Enhancements

Revise Class Factors Revise/Enhance Territories

– May have impact similar to Homeowners on Protection

Create more sound AOI curve Develop Underwriting Scorecard Incorporating

– Credit scores– Other “who” characteristics

(especially those already required on the application)

The GLM Approach

Capture by Policy and by Claim Experience Append Credit Variables and Application Data Develop Frequency and Severity Models

– Consider Relevant Interactions

Combine Models to Develop Pure Premiums and Tiering Plan/Scorecard Elements Simultaneously

Underwriting Scorecard Example

Underwriting Score Points - D&B Financial Assessment

Strength High Good Fair Limited5A 250 250 200 1504A 250 250 200 1503A 250 250 200 1502A 250 200 150 1001A 250 200 150 100BA 250 200 150 100BB 250 200 150 100CB 200 200 150 100CC 200 200 150 100DC 200 150 100 50DD 200 150 100 50EE 200 150 100 50FF 200 150 100 50GG 200 150 100 50HH 200 150 100 50

Absence 250 200 150 100

Composite Credit Appraisal

Underwriting Scorecard Example

Years of PercentCurrent Score Building ScoreControl Points Occupied Points

>10 150 >95% 1006-10 75 65-95% 500-5 0 <65% 0

Part Time/ Score Safety ScoreFull Time Points Program Points

<33% 50 Formal 5033% - 67% 25 Informal 25

>67% 0 None 0

Building < 25 Yrs Old 25 Pts Owner on Premises 15 PtsCentral Alarm 25 Pts Franchise 10 PtsNo Parking Lot 10 Pts Closed by 9 pm 10 PtsOffsite EDP Backup 5 pts No Delivery 5 pts

Underwriting Scorecard Example

Cumulative TieringPoint Range Factor

0 - 99 1.00100 - 199 0.92200 - 299 0.84300 - 399 0.76400 - 499 0.68500 - 599 0.60600 - 700 0.52

Issues of Developing BOP U/W Scorecard

Concerns Over Use of Credit “No Hits” Capture of Application Data Volume of Data for Certain Classes, Territories,

etc.

Benefits of Using GLM for BOP Pricing Enhancements

Reduce Reliance on Underwriting Discretion Improves Predictive Accuracy Creates Adverse Selection for Competitors Reflects Interactions with Between Rating

Factors

Credit’s Problem - Interactions

1.70

1.32

1.19

1.04

0.86

0.73

1.51

1.24

1.12

1.00

0.90

0.81

-

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

1 2 3 4 5 6

Level

Ind

ica

ted

Ra

te D

iffe

ren

tia

l

Loss Ratio

GLM

“There’s always a greater fool”

Insurer Soft Quote Sucha Quote

Farm States $1,900 $1,700

Inilli Mutual $1,800 $1,900

OSInsCo $2,100 $2,100

Yankees Ins Co. $1,500 $2,400

Naïve Capital $1,350 $2,720

Adverse Selection

Insurer

Soft Quote

Soft Loss Ratio

Sucha Quote

Sucha Loss Ratio

Farm States $1,900 51% $1,700 92%

Inilli Mutual $1,800 54% $1,900 82%

OSInsCo $2,100 46% $2,100 74%

Yankees Ins Co.

$1,500 65% $2,400 65%

Naïve Capital $1,350 72% $2,720 57%

The Impact of Credit and Other Factors May Vary by Class

Pure Premium Relativities by Program and Years in Business

0-3 4-6 7-10 10+

Years in Business

Pu

re P

rem

ium

Re

lati

vit

y

Contractors

Habitational

Office

Restaurant

Retail/Service

Wholesale

Underwriting Scorecards Reflecting Interactions

Multivariate analysis allows the modeling of interactions and modern policy management systems facilitate the implementation of more complex tiering systems

Years ofCurrentControl Contr. Habit. Off. Rest. Ret./Serv. Wholes.

0-3 60 115 120 70 95 1004-6 100 130 125 85 100 110

7-10 120 135 135 100 120 12510+ 150 150 150 150 150 150

Score Points

Parting Thoughts

Where there is no vision, the people perish. – Proverbs 29:18

The data’s ready,The technology’s ready,

ARE YOU READY???