Presentation given at the 2015 SOA Annual Meeting by Barry Senensky

1. Predictive
Modeling
in
Underwriting
BARRY
SENENSKY
FSA,
FCIA,
MAAA
14
Oct
2015
1

2. Agenda
1. Why
has
it
taken
so
long?
2. Predictive
Modeling
Approaches
3. Data
Sources
4. Building
the
Predictive
Model
5. Summary
2

3. Why
has
it
taken
so
long?

4. A
long
time
coming…
Predictive
Modeling
has
been
used
in
Industry
for
50+
years
Predictive
Modeling
has
been
used
by
P&C
Insurers
for
20+
years
Predictive
Modeling
has
been
used
for
scoring
Disability
Claims
on
the
likelihood
of
recovery
for
over
10
years
So
why
not
Underwriting?
1. Life
Insurance
business
is
conservative/slow
to
change
2. Results
take
5-­‐10
years
to
become
apparent
4

5. So
why
now?
Availability
of
Data
and
CPU’s
to
process
the
data
Fits
well
with
Online
Insurance
Sales
where
companies
are
looking
for
less
expensive,
less
intrusive
and
quicker
ways
to
sell
insurance
policies
Just
makes
too
much
sense
5

6. Predictive
Modeling
Approaches
6

7. Predictive
Modeling
Approaches
1. Replicate
Current
Underwriting
Decisions
2. Model
mortality
rates
directly
for
unique
applicants
7

8. Data
Sources
8
1. Internal
2. Third
Party
3. Customer’s
own

9. Internal
Data
Sources
1. Data
Collected
from
current
underwriting
practices
2. Application
• Provides
good
underwriting
information
• May
have
material
inaccuracies
3. Fluids
and
other
medical
tests/information
• Provides
good
underwriting
information
• Slow
and
expensive
to
collect
• Poor
customer
experience
9

10. Third
Party
Data
Includes
data
about
an
individual
obtained
from
a
third
party
including
data;
• Purchased
from
data
aggregator
such
as
LexisNexis
• Purchased
from
another
company
that
has
the
individual
as
a
customer
such
as
a
pharmacy
or
telecommunications
provider
• Scraped
off
the
web
such
as
from
Linked
in
or
Facebook
10

11. Third
Party
Data
Advantages
• Quick
to
obtain
• Low
cost
• Physically
Non-­‐invasive
Concerns
• Reliability
and
completeness
of
data
• Customer
Privacy
11

12. Customers’
Own
Data
• Includes
data
collected
from
EHR’s,
wearable
devices
and
wellness
programs
• Early
indications
are
positive
for
Auto
Insurance
• Skeptical
of
value
in
near
future
for
Life
Insurance
Underwriting
12

13. Building
the
Predictive
Model

14. Two
Possible
Approaches
1. Replicate
current
underwriting
decisions
2. Model
mortality
rates
directly
for
unique
individuals
14

15. Replicate
Current
Underwriting
Decisions
Possible
Objectives
• Enhance
consistency
of
decisions
between
underwriters
• Identify
predictive
data
fields
• Replace
existing
process
with
one
that
is
quicker
cheaper
Advantages
• Historical
experience
is
not
required
• Fairly
straightforward
to
develop
Issues
• Maintains
but
does
not
improve
underwriting
decisions
• Issue
of
how
to
keep
current
over
time
15

16. Modeling
Mortality
Rates
Directly
Objectives
• Identify
predictive
data
fields
• Replace
existing
process
with
one
that
is
quicker
and
cheaper
• Predict
applicant
specific
mortality
rates
Advantages
• Should
improve
underwriting
decisions
and
profitability
of
business
Issues
• Need
historical
experience
for
all
applicants
• How
do
you
get
vital
status?
• Many
modeling
issues
16

17. Modeling
Mortality
Rates
Directly-­‐
Modeling
Issues
• Build
a
model
from
scratch
or
start
with
a
standard
table?
• How
many
years
from
issue
do
we
model?
Then
what?
• How
do
we
incorporate
mortality
improvement?
17

18. Smaller
Company
Issues
• Accumulating
large
enough
data
sets
to
build
credible
models
• Higher
unit
cost
of
building
infrastructure
18

19. Ongoing
Management
• Need
to
periodically
refresh
models
• Predictive
models
are
good
at
assessing
benefits
of
questions
on
applications
and
medical
tests
19

20. Summary
20

21. Summary
• Predictive
Modeling
in
Underwriting
has
arrived
• If
you
haven’t
done
so
yet;
ØNeed
to
decide
how
you
want
to
incorporate
into
your
underwriting
process
Øidentify
and
start
collecting
relevant
data
21

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