Transcript Innovative Approaches in a Tough Market

Taking Underwriting to the Next
Level with Predictive Analytics
Innovative Approaches in a Tough Market
Hansong Choi
Underwriting Strategist/
Data Specialist/Modeler
Prudential of Korea
Nitin Basant
Analytic Science
FICO
© 2014 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
The Power of Predictive Analytics
How can predictive analytics help transform
underwriting in the insurance industry?
© 2014 Fair Isaac Corporation. Confidential.
Agenda
►Introduction
►Life
Insurance Market in Korea
►An
Utilization of Predictive Model in
Underwriting
► EUS
(Expert Underwriting System) Model
► Preferred Underwriting Model
► Tele Interview Model
►New
Ideas
© 2014 Fair Isaac Corporation. Confidential.
Introduction
► Who Are
We?
© 2014 Fair Isaac Corporation. Confidential.
Introduction
► Prudential
of Korea
Asset
Total asset of $US 11.2 billion
Net profit of $US 183.6 million
RBC
The highest level of RBC Ratio
in the industry = 432.2%
CSI
© 2014 Fair Isaac Corporation. Confidential.
MDRT 92.3%, CIC 35%
13th Persistency rate 87.9%
Life Insurance Market in Korea
© 2014 Fair Isaac Corporation. Confidential.
Life Insurance Market in Korea
► Enhanced
Financial Supervisory
Services (FSS) regulatory
oversight
The size of Korea life
insurance market
91.2 billion USD, M/S 3.5%
Global rank 8th
► Blind
spot of National Health
Insurance
Medical cost
Personal
Burden
► Intensified
© 2014 Fair Isaac Corporation. Confidential.
National health
insurance
moral hazard
Feature of Underwriting
Introduction of
predictive modeling
No discrimination
of underwriting
Limited collection of
personal information
The claims paid by
insurance fraud is
estimated $US 3.4 billion
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Utilization of Predictive Models in
Underwriting
► EUS
(Expert Underwriting System) Model
© 2014 Fair Isaac Corporation. Confidential.
EUS Model
EUS (Expert Underwriting System)
► Development
Objectives
Target
Method
Strengthen loss ratio
management
Build risk DB, predictive model
Enhance underwriter’s
professionalism
Improve underwriting efficiency + rule set compliment
Objective
2011
Introduction
Risk DB and predictive model
N
Y
Automated Issue Ratio
8%
45%
© 2014 Fair Isaac Corporation. Confidential.
EUS Model
EUS (Expert Underwriting System)
► Development
review
BRMS(Business Rule Management System)
How to handle the policies?
(Operation’s aspect)
FICO® Blaze Advisor
Predictive Model
There are subsidiary
Information
FICO® Model Builder
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EUS Model
EUS Process and Utilization of Model
Operation Work
with tiny risk
Agency
Score
Validator
Medical Examination
Tele Interview
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Underwriter
Death Score
Surgery Score
Hospitalization
Etc.
Risk
Mart
Predictive
Model
DB
EUS Model
Predictive Model Development
 DB upgrade
Risk
Mart
risk
factor1
risk
factor2
risk
factor3
risk
factorN
Defining a target
incidence within 3 years
© 2014 Fair Isaac Corporation. Confidential.
 GAM
(Generalized Addictive Model)
Modeling
algorithm
Predictive
Model
(scoring)
 Death
Surgery
Hospitalization
:
Apply to
new business
underwriting
 Focus underwriting on
high risk (score) cases
EUS Model
Definition of Hospitalization Coverage
Coverage: all of hospitalization in terms of disease and accident
► Guaranteed
►3
amount: KRW 10 thousand won(10$) per a day
days contestable period, maximum 180 days
► *5,000
face amount = 5 x 10 thousand won = 50 thousand won (10$) per a day
© 2014 Fair Isaac Corporation. Confidential.
EUS Model
Utilization of EUS Model
Hospitalization Score grade
Low
Risk
► Give
Automatic underwriting
► Enlarge maximum face amount
► Mitigate financial/medical
underwriting
2
3
25%
21,4%
4 ► General Underwriting
Selective tele Interview
Expected morbidity ratio within 3 years
15,0%
14,0%
10%
7,5%
6,2%
5,6%
© 2014 Fair Isaac Corporation. Confidential.
for all policies
7,4%
5,4%
3,2%
2,6%
2,2%
6
5
4
6
► Inspection
11,3%
9,2%
5%
7
(Cumulative Morbidity ratio)
After 2 years
11,1%
5
High
Risk
After 1 year
20,5%
20%
15%
►
ratio of accepted policies
preferential treatment
►
1
► Morbidity
5,3%
3,8%
2,9%
1,7%
1,4%
0,0%
2,3%
0%
7
3
2
1
Total
EUS Model
Effect of Scoring Model Introduction
► The
underwriters are starting to pay attention to not only medical information,
but also non-medical information
Medical
Examination
High Risk
Financial
inquiry
Tele
Interview
Review
All paid out
claim
© 2014 Fair Isaac Corporation. Confidential.
Low Risk
Automated
Underwriting
EUS Model
Benefit of Score Model
► A&H
Loss ratio
<A&H Loss ratio by coverage(≤ 2 years)>
120%
CY'10
CY'11
CY'12
CY'13
100%
80%
60%
40%
20%
0%
Surgery
© 2014 Fair Isaac Corporation. Confidential.
Hospi.
Cancer
CI
TOTAL
EUS Model
Utilization of EUS Model
Projection
Score
Underwriting
Accept
Loss
Ratio
Morbidity
Ratio
Score
Risk Factors
1. Agent’s hospitalization loss
ratio for 1 year
2. Region
3. Occupation section
4. Insured age
5. Gender
© 2014 Fair Isaac Corporation. Confidential.
Underwriting Effects
Reject
How did you
reject low
score?
Reject policy’s
Morbidity & Loss
ratio projection
Utilization of Predictive Models in
Underwriting
► Preferred
Underwriting
© 2014 Fair Isaac Corporation. Confidential.
Preferred Underwriting
Preferred in Korea Market
► Preferred
Condition in Korea
As a person can apply base plan…
Smoking Habit
►Non-smoker
for
the last 1 year
Blood Pressure
►Systolic
Pressure
110~139
All conditions should be met
© 2014 Fair Isaac Corporation. Confidential.
BMI
(Body Mass Index)
►Weight(Kg)
Height(m²)
►20–27.9
/
Preferred Underwriting
Why does Preferred Insured Show Higher Loss Ratio in Every Years?
► Loss
ratio
60%
50%
40%
34%
26%
30%
20%
10%
0%
Preferred Insured
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Standard Insured
Preferred Underwriting
Factor Selection
► What
kind of factors affect death coverage?
Factors
(= Information)
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Preferred
Preferred Underwriting
Preferred Differentiation Method
► How
to choose preferred insured
► Should
►
meet all
conditions
Knockout
Point
Current
Korea
© 2014 Fair Isaac Corporation. Confidential.
Meet at least several
items out of all
conditions
► Reaching
a certain
score that gives a
specific item’s weights
and score
Debit/Credit
Preferred Underwriting
We Knew Lifestyle Shows a Greater Impact than Physical Condition
© 2014 Fair Isaac Corporation. Confidential.
Preferred Underwriting
How Much Weight Was Given?
© 2014 Fair Isaac Corporation. Confidential.
A Utilization of Predictive Models in
Underwriting
► Tele
Interview Model
© 2014 Fair Isaac Corporation. Confidential.
Selective TI (Tele Interview) Model
Background
► In
Korea,
Only specified information can be collected.
Cannot be rejected as a direct cause of MIB
and RX profile, MVR report.
So, some information from customers should be collected in order to underwrite
through Tele Interview
© 2014 Fair Isaac Corporation. Confidential.
Selective TI (Tele Interview) Model
Who can be target to call?
Targeting
Predictive Modeling
Information
28
© 2014 Fair Isaac Corporation. Confidential.
Selective TI (Tele Interview) Model
Definition of Target
Hit ratio
Medical rejection + extra charge+ exclusion rider + reduced Face amount
Tele Interview
Target
© 2014 Fair Isaac Corporation. Confidential.
= 15.3%
Effective Tele Interview
► Selective
►
TI
Target Ratio
Insured Age
General Death Benefit Amount
≤40
≤50
≤60
>60
≤10 mil.
>10 mil.
>30 mil.
Non-medical Exam
Selective TI
>50 mil.
Tele Interview
>100 mil.
>200 mil.
>15 bil.
© 2014 Fair Isaac Corporation. Confidential.
30%↑
(Step 1)
Special
Exam C
>300 mil.
>13 bil.
Special
Exam A
4.3%
Special
Exam B
0.6%
4.0%
>7 mil.
50%↑
(Step 2)
91.8%
0.1%
Special
Exam D
0.1%
15.3%
(Now)
Special
Exam E
0.0%
Selective TI (Tele Interview) Model
Modeling
Variable
Apply신청여부
preferred
우량체
Apply preferred y/n
0,4017
Number of riders
0,3193
Sum of general death
benefit amount
Hit history
0,1798
0,0539
Elapsed period
Product Category
Replacement contract Y/N
Insured Age
0,043
0,0317
0,0157
© 2014 Fair Isaac Corporation. Confidential.
Unscaled
39
0.319
[-, 1)
-21
-0.242
-1.411
[1, 2)
-34
-0.341
[2, 3)
-2
-0.031
[-, 2)
-110
-0.889
[3, 7)
-3
-0.01
[2, 3)
-9
-0.073
[7, 10)
16
0.184
[3, 5)
53
0.427
[10, +)
25
0.229
[5, +)
74
0.596
Product
상품종류 Category
-86
-0.707
63
0.508
TERM
-8
-0.096
5
0.038
WHOLELIFE
4
0.028
[150000001, 250000000)
-35
-0.281
36
0.369
[250000000, +)
-78
-0.629
-10
-0.078
No
84
0.675
Yes
(-, 20000)
-79
-0.638
[-, 31)
[20000, 35000)
-31
-0.247
[31, 38)
2
0.006
[35000, 55000)
-5
-0.044
[38, 42)
1
-0.004
[55000, 115000)
18
0.145
[42, 47)
-7
-0.07
[115000, 290000)
18
0.146
[47, 52)
0
0.018
[290000, +)
-1
-0.008
[52, +)
27
0.294
Sum
of general
Death
Benefit amount
기계약합산
일반사망
가입금액
RI
CHILD DUHC FAMILY_INCOME MULTI_PLUS
PEN_SAV
Replacement
contract Y/N
대체계약여부
Hit History
적출이력
N
없음
Y
있음
0,0378
Scaled
Elapsed
period
POK 최초 청약후
기간(년)
-175
[50000001, 150000001)
0,0498
Variable
Unscaled
N
없음
Y(Preferred)
우량체
Number
특약건수 of riders
(-, 50000001)
Real premium
Scaled
y/n
3
0.028
-125
-1.249
-25
-0.232
Insured
가입연령 Age
Real Premium
실납입보험료
Selective TI (Tele Interview) Model
► Roc
curve
© 2014 Fair Isaac Corporation. Confidential.
► Model
Result
setID
Score Variable
train
Model_score
test
Model_score
Divergence
1.127
1.058
AUC
KS
KS percentile
0.769
41.529
62.70%
0.762
40.094
61.40%
Selective TI (Tele Interview) Model
► Total A&H
loss ratio (≤2 years)
100%
►
Target ratio
35%
90%
30%
80%
70%
25%
60%
20%
50%
15%
40%
10%
30%
20%
5%
10%
0%
Before 2014
0%
CY'10
CY'11
© 2014 Fair Isaac Corporation. Confidential.
CY'12
CY'13
2014.1Q
2014.2Q
2014.3Q
New Ideas
© 2014 Fair Isaac Corporation. Confidential.
New Idea
Underwriting Model
Simplified
issue
Fraud
detection
Preferred
Medical
Exam
Extra
Charge
Loss ratio
© 2014 Fair Isaac Corporation. Confidential.
Inforce
Marketing
Thank You!
Hansong Choi
[email protected]
© 2014 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
Learn More at FICO World
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© 2014 Fair Isaac Corporation. Confidential.
Please rate this session online!
Hansong Choi
[email protected]
© 2014 Fair Isaac Corporation. Confidential.
Nitin Basant
[email protected]
# Appendix: Selected Factors by Coverage
EUS (Expert Underwriting System) Model
Death
1. Agent’s total loss ratio for
1 year
2. Contractor=Beneficiary Y/N
3. Insured age
4. Occupation section
5. Disease diagnosis below
5 years
6. Region
Disability
1.
2.
3.
4.
5.
Gender
Agent’s loss ratio for 1year
Change job within 2year
Job
LP loss ratio fro 1year in terms
of accidental death
6. Hospitalization, surgery rider
y/n
Surgery
1.
2.
3.
4.
LP loss ratio for 1year
Age
Gender
Cumulative surgery
Face amount
5. Number of no-warrant contract
© 2014 Fair Isaac Corporation. Confidential.
CI
1.
2.
3.
4.
5.
6.
Gender
Diagnosis History ≤ 5 years
Age
Voluntary contract
LP Job grade
BMI
Cancer
1.
2.
3.
4.
5.
6.
7.
8.
9.
Age
LP loss ratio for 1year
Gender
Contract date
Cancer rider y/n
Relationship of policy owner and insured
Job
Notice Examination within 5 years
LP loss ratio for 1year
Hospitalization
1. LP Hospitalization loss ratio
for 1 years
2. Region
3. Occupation section
4. Insured age
5. gender
Etc.
1.
2.
3.
4.
Region
Notable LP
Gender
Relationship of policy owner
and insured
5. LP Job grade