managing data (big and small) for analyticsmanaging data (big and small) for analytics june 11, 2015...

Managing Data (Big

and Small) for Analytics

June 11, 2015

1

Importance of Predictive Analytics

• Predictive Analytics can help insurers be more

effective in all segments of the value chain – Marketing – Target and acquire the right customers

– Actuarial – Prices that accurately reflect risk

– Underwriting – Select the proper risks and proper

products

– Claims – Identify suspicious claims

• The industry is getting more competitive – Top 10 personal auto insurers had 1/2 the market share

in 1980; now they have 2/3 of the market share

– Only the “fittest” will survive; analytics can provide the

needed competitive advantage

– The industry has recognized the value of analytics

2

• Predictive analytics is used

most often in personal lines.

• 100% of the larger personal

lines insurers we surveyed use

predictive analytics!

• Of course, personal lines

(and PL auto, in particular) is

the largest and one of the

most competitive segments

of the P&C market. Insurers

are looking for any

competitive edge they can

find.

Who Uses Predictive Modeling?

Use of predictive analytics by

size of personal lines book

Source: ISO and Earnix 2013 Insurance Predictive Modeling Survey

• Pricing is the most common

use of predictive modeling.

• A majority of insurers also use

predictive modeling for

underwriting at least

frequently.

• But there is still significant

usage in marketing, claims,

and reserving.

How Insurers Use Predictive Modeling

Predictive modeling use by

function

42%

20% 9% 7% 9%

39%

32%

18% 14% 9%

13%

21%

14% 18%

11%

4%

9%

12% 14%

17%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Rarely

Occasionally

Frequently

Always


• Lack of sufficient data is the

biggest challenge – both

quantity and scope.

• Lack of skilled modelers

is a close second most

challenging factor for those

building an internal

predictive modeling

capability.

Predictive Modeling Challenges

Predictive modeling challenges

9%

6%

23%

31%

47%

53%

Other

We have no challenges

Need better modeling tools

Need additional computingresources

Not enough skilled modelers

Lack of sufficient data


• Data is a challenge for

everybody, but large and

small insurers have different

challenges.

• Larger insurers are most

concerned with data quality.

• Smaller insurers don’t have

enough observations.

Data Challenges

Data challenges by company size

42%

17%

18%

6%

7%

6%

30%

57%

3% 13%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

<$1B >$1B

Other

Data is not clean,tough to use

Data is not current

Not enough variabilityin the data

Not enoughobservations

Numbers may not add up to 100% due to rounding


• More than 90% of insurers

supplement their internal

data with one or more types

of third-party data.

• The most common data

types are credit-related data

and geo-demographic

data.

Third-Party Data

Types of third-party data used

5%

9%

42%

46%

53%

67%

80%

Other

3rd party telematics data

Weather data

Catastrophe models data

Competitive pricing data

Geo-demographical data

Insurance score or raw creditattributes


• Preparing data for analysis is

a major bottleneck and

drain on resources for most

insurers.

• 54% of insurers typically

spend more than 3 months

to prepare their data for a

project.

Data Preparation

Data extraction and preparation time

18%

28%

37%

17%

Less than 1month

1 – 2 months 3 – 6 months More than 6months


• The insurance industry,

especially many smaller

insurers, have not yet figured

out how to take advantage

of Big Data.

• Once new big data

technologies have been

used to extract the useful

“nuggets” from the new, vast

data streams, the data

management tasks are

similar to other types of

analytic data.

Big Data

Role of Big Data in Modeling Initiatives


7% 17%

23%

34% 8%

12%

62%

37%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

<$1B >$1B

Doing nothing withBig Data

Would like to takeadvantage of Big Data,but it is costprohibitiveEvaluating/implementing the use of Big Data

Use Big Data and haveincorporated it in ourmodels

Good Data: Good Analytics

Good quality data can often compensate for

mediocre analysis …

… but, the reverse is never true.

No matter how skilled the analyst, …

… bad data will always lead to bad results!

10

Data Use for Analytics is Different

• Some Characteristics of Analytics Use of Data

– Sophisticated Users

– Ad Hoc & Iterative

– Repurposed Data

– Granular and Denormalized Data

– Data Quality and Metadata are different

11

• Most modelers will have advanced degrees

in Statistics, Economics, Applied

Mathematics, etc.

• Users will be looking at the data from new

perspectives and using the data in new ways

• Can lead to new insights into the data for

data owners – can also cause friction.

• Data managers need to understand the

predictive analytics process

Sophisticated Users

12

Predictive Analytics Process Overview

13

Data

Business

Understanding

Data

Understanding

Data

Preparation

Modeling

Evaluation

Deployment

CRISP – DM: Cross-Industry

Standard Process for Data Mining

• Analysts will design their queries as needed

• The iterative nature of the analytic process

means the analyst will be back again and

again for more and different data

• There is no “standard” analytics data report

that can be specified when designing the

data resource. Structure needs to efficiently

and flexibly support ad hoc quiries.

Ad Hoc Nature of Data Access

14

• Rarely are the operational data stores collected into a single “Enterprise Data Warehouse” – You will need to create a useful analytic data store

• Even more rare, is data that has been collected specifically for analytics – usually, analytics is an opportunistic user of data that has been collected for other purposes – Data will need to be cleaned, transformed,

conformed, and documented before it is certified as “fit for use” for analytics and included in the analytic data store

Repurposed Data

15

• Insurance companies collect vast quantities of data in the course of business

• Typical Insurance Analytics Data Sources – Customer Relationship Management – Quoting/Underwriting – Policy Management – Billing – Claims – Audit – Actuarial Research – Financial Reporting – Publically Available Data – Third-party data vendors

Insurance Company Data Sources

16

• End goal – Always remember the goal – Two-dimensional flat file for input into modeling

software

– Each record contains an identifier, candidate

predictor variables for testing, and one or more

target variables

• Analysis requires historical data and the

vintage of the predictor variables must be

matched to the target variables

• Must support the granularity required for level

analysis

Granular and Denormalized Data

17

• What does each record represent? • Common record types for insurance analytics

– Customer-related • First named Insured • Household • Quote

– Policy – Coverage – Claim – Geography

• Census tract • County • State • Underwriting Territory • Zip Code

Granularity

18

• Star Schema is often adopted to support analytics

• Data will often be denormalized and aggregated from source systems

• Analytic databases will often grow to contain more history than the source systems. Plan for growth.

• Every variable needs a vintage • Indexing needs will be imperfectly defined. Count on supporting multiple table joins from any direction = many indexes.

• Granularity – pick the lowest level as your base – This means more data, but … – … it is the most flexible design. Data can usually be

aggregated to a higher level of granularity but you can never go below your base level.

Implications for Analytic Database Design

19

• Important Data Quality measures for Analytics: – Accuracy – how well does data match reality?

– Reliability – is measurement repeatable and consistent?

– Timeliness – is data available at time of prediction?

– Completeness – is data available for most cases?

– Permissibility – can you use the data as intended?

• Analysts usually can’t control the quality of the data when acquired. So, they must at least

know the quality of the data in order to

determine the usefulness of the data.

• Metadata – documentation of this information

Data Quality and Metadata

20

• Advanced analytics requires different data

management support than most other uses of

the data

• Two broad areas that demonstrate those

needs: – Database design

– Data quality and metadata

• Strive to build an analytic data store that

considers the unique needs of analytics.

Analytic Data Management Summary

21

The great big data debate

• Meaning of big data

• The ROI question

• Finding the right data for the problem*

• Challenges and approaches

• Emerging data sources

* - … or, finding the right problem for the data

22

The definition of big data is big data

23

The broad

range of

new and

massive

data types

that have

appeared

over the last

decade or

so. – Tom

Davenport,

Big Data @

Work

The ability of society to harness information in novel ways to

produce useful insights or goods and services of significant

value” and “…things one can do at a large scale that cannot

be done at a smaller one, to extract new insights or create

new forms of value.. – Viktor Mayer-Schönberger and

Kenneth Cukier Datasets whose

size is beyond the

ability of typical

database software

tools to capture,

store, manage,

and analyze --

McKinsey

Data of a very large size, typically

to the extent that its manipulation

and management present

significant logistical challenges. –

Oxford English Dictionary

An all-encompassing term for any collection of data sets so large and complex

that it becomes difficult to process using on-hand data management tools or

traditional data processing applications. – Wikipedia

Source: 12 Big Data Definitions: What’s Yours?

http://www.forbes.com/sites/gilpress/2014/09/03/12-big-data-definitions-whats-yours/

Volume

Velo

city

Variety

Vera

city

Top twenty words across definitions

24

Examples of big data in P&C today

25

• Aerial

• Business

• Connected home

• Consumer

• Econometric

• Financial

• GIS

• Government

• Psychographic

• Retail

• Social media

• Topographic

• Traffic Cam

• Vehicle Build

• Vehicle Telematics

• Weather

26

The ROI question

• Aerial

• Business

• Connected home

• Consumer

• Econometric

• Financial

• GIS

• Government

• Psychographic

• Retail

• Social media

• Topographic

• Traffic Cam

• Vehicle Build


• Weather

Driving behavior data

27

2013/08/18

22:47:53.07 UTC

34° 59’ 20”

-106 ° 36’ 52”

-9.8

m /s

2

3.27 Gal / fuel

256.6°F

4200 RPM

72,852 Miles

Dr. Seatbelt: Y

Interstate 40

(Freeway)

Speed Limit

65 MPH

Albuquerque,

New Mexico

101°F

25 Mil Vis

Wind: 2mph NW

Sunny

Simulating the ROI on UBI

28

ROI

Selection

Driving

Improve-

ment

Ancillary

Revenue

vs. Cost

Savings

Reunder

-writing

Technology

Incentives

and Rewards

Analytics and

Models

Logistics

Partners and

Services

Elasticity

and Cost

Mix of

Business

Competitive

Landscape

Regulation

Distribution

Anti-selection example

29

All values are hypothetical and illustrative. In the example, policyholders

switch to insurers with UBI if they can find a rate 25% lower.

Avg Pure Loss

Year Rate Danny DJ Michelle Jesse Joey Prem Ratio

1 800 228 423 520 618 813 520 65%

2 800 X 423 520 618 813 593 74%

3 913 X X 520 618 813 650 71%

4 1000 X X X 618 813 715 72%

5 1100 X X X 618 813 715 65%

884 611 69%

© Insurance Services Office, Inc., 2015

30

How much does the model matter

-75.0%

-50.0%

-25.0%

0.0%

25.0%

50.0%

1 2 3 4 5 6

Constant Learning

-75.0%

-50.0%

-25.0%

0.0%

25.0%

50.0%

1 2 3 4 5 6

Constant Learning

-75.0%

-50.0%

-25.0%

0.0%

25.0%

50.0%

1 2 3 4 5 6

Constant Learning Graduated Learning

Hypothetical

Five Year

Annualized ROI

Model Power

(High Tertile ÷

Lower Tertile )

“Common Dongle / Book of Business” Assumption Set

• Approximately industry average premiums / expenses

• $100 hardware, $5 monthly wireless

• Three year useful life

• Three vehicles per year

• 10% annual cost reductions


Example of learning

31

0

10

20

30

40

50

60

70

0 5 10 15 20

Initially Safest Initially Average Initially Riskiest

ISO

Safe

ty S

core

Weeks of Driving

UBI Score by Week of Driving

Incre

asin

g R

isk

Source: Sample of over 1,000 private passenger vehicles observed in late 2014


Monte Carlo simulation

32

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

Five year return on investment (ROI)

Assumes common dongle cost structure, device deployment to three

vehicles per year in typical book, and 3x model power.

Perc

enta

ge o

f S

imula

tions


33

Finding the right data for the problem

• Aerial

• Business

• Connected home

• Consumer

• Econometric

• Financial

• GIS

• Government

• Psychographic

• Retail

• Social media

• Topographic

• Traffic Cam

• Vehicle Build


• Weather

• Carrier XYZ

• 2007 $80M DWP

• 2007 Loss Ratio: 65.8%

• Carrier charges single rate

for entire territory

• Does not believe in granular

rating, so does nothing while

competitors implement

granular rates

Location of policies for a single territory

Best Practice Case Study: Granular Rating

34

• Carrier XYZ (4 Years Later)

• Competitors have cherry-

picked best risks, leaving

XYZ with concentration of

poor risks

• 2011 $75M DWP

• 2011 Loss Ratio: 72.7%

• Down $5M in premium

• Loss Ratio Up 6.9 points

• Profits down $7M

Location of policies for a single territory

Best Practice Case Study: Granular Rating

35

Going granular

36

Territories: 1 ZIP Codes: 34 Block Groups: 669

Example: Milwaukee, Wisconsin, Geographic Area

Environmental Risk Factors for Auto

Importance of Peril

34%

66%

2012

28%

72%

2007

Non-By-

Peril

Insurers

Loss Ratio

76.6%

25 By-Peril

Insurers’

Loss Ratio

69.2%

Estimated

Market

Share DWP

(A.M. Best)

Same age, ZIP, age, PPC: same risk?

Traditional approach: these homes may all be rated similarly based on the amount of insurance

Attribute approach: roof materials, HVAC, number of bathrooms, basements, garages etc. make a difference – esp. by peril

Building characteristics approach

40

Brickface, attached garage ↓ wind exposure

Larger floor plan, carport ↑ wind exposure

Fireplace ↑ Higher hail exposure

Composite shingles ↓ hail exposure

3½ bath, two stories ↑ water (non-weather) exposure

Single story, pool ↑ theft/vandalism exposure

41

Challenges and approaches

• Aerial

• Business

• Connected home

• Consumer

• Econometric

• Financial

• GIS

• Government

• Psychographic

• Retail

• Social media

• Topographic

• Traffic Cam

• Vehicle Build


• Weather

Data bucket challenge

Wet Weather

… eaking [sic] ice maker in bar …

after heavy downpour, insured noticed water damage to ceiling and walls in den

… freeze damage to swimming pool …

… freezer defrosted and water leaked …

Wet, not Weather

Fuzzy matching example

43

Model Years Size Body Vehicle

2011-2013 Small Two-door Car Honda Civic

2011-2013 Small Two-door Car Honda Civic Si

Year Make Model

2013 Honda Civic 2DR FWD

Year Make Model

2013 Honda Civic 2-door coupe

Class Make Model

Small Family Car Honda Civic

Small Family Car Honda Civic Hybrid

Year Make Model Style

2013 Honda Civic EX

Sources:

Cars.com

Edmunds.com

Euroncap.org

Iihs.org/iihs/ratings

Iihs.org/iihs/topics/insurance-loss-information

Safercar.gov

Year Make Model Configuration

2013 Honda Civic Coupe EX

© In

su

ran

ce

Se

rvic

es O

ffice, In

c. 2

01

5

44

VIN

ABC

Symbol approaches -- attributes

Relative

Frequency

and

Severity

Covariates

Territory, Operator Age, Marital Status,

Driving Record, Insurance Score, Limits,

Deductibles, Affinity

Manu-

facturer

Data

Ratings

and Tests

Car

Gurus

Econo-

metrics

Wheelbase, Height, Weight, Body Style,

Engine Size, Horsepower, Airbags

e.g. Crash Tests

e.g. Braking

Distance

e.g. model year

CPI or KBB

© Insurance Services Office, Inc. 2015

45

VIN

ABC

Symbol approaches -- attributes

Relative

Frequency

and

Severity

Covariates

Territory, Operator Age, Marital Status,

Driving Record, Insurance Score, Limits,

Deductibles, Affinity

Manu-

facturer

Data

Ratings

and Tests

Car

Gurus

Econo-

metrics

Wheelbase, Height, Weight, Body Style,

Engine Size, Horsepower, Airbags

e.g. Crash Tests

e.g. Braking

Distance

e.g. model year

CPI or KBB


Potential decision tree – theft claims

46

Anti-

Theft

Alarm

Yes

No

Body

Style

Cool

Uncool

SVR

System

Yes

No

Frequency 2%

Frequency 8% Frequency 1%

Frequency 0.25% Frequency 7%

Frequency 9.75% Frequency 11.5%

Frequency 2.5%

Frequency 22.75%

Frequency 4.75%

Frequency 1.75%

Frequency 13.75%

NOTE: These results are hypothetical. Please do not reproduce. © Insurance Services Office, Inc. 2015

Comparing approaches

47

Experience Attribute

Speed Trailing indicators

Leading indicators

Granularity Reliant on MSRP w/in series Trim level predictions

Objectivity Intangibles and evolution Defined set of attributes

Maintenance

Annual review Resolution and remodeling

Accuracy High at series level but

limited within series

Limited for variations beyond

modeled set of attributes


Best of all worlds

48

Mixer

Best Estimate


49

Emerging Data Sources

• Aerial

• Business

• Connected home

• Consumer

• Econometric

• Financial

• GIS

• Government

• Psychographic

• Retail

• Social media

• Topographic

• Traffic Cam

• Vehicle Build


• Weather

Speed kills

50

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400

Day of observation period ending April 3, 2015

Num

ber

of T

weets

per

day

Twitter Universe's Reaction to Autonomous Cars

3/18/2015 Tesla

teases self-

driving software

1/6/2015 Ford

announces five-

year plan, Benz

concept 7/27/2014 Baidu

announces auto-

nomous concept

3/16/2015

Coast-to-

coast

experiment

10/21/2014 Audi

sets speed record

for autonomous

cars

5/29/2014 Google

reveals “no steering

wheel” concept

Big data fits on a single page

51

Groups

Toastmasters

Straphangers

Woodstock Revival

Slopes Loyalty Program

Tapas for the Road

Work History

Employer: Insuranceplex

Position: Actuarial Sensei

Tenure: 1974 – Present

Recent Activity

Basic Info

Gender: Male

Birthday: MYOB

Hometown: Island

Paradise

Status: Happily

Unmarried

Contact Info

Email: [email protected]

Phone: 123.456.7890

Personal Information

Activities: Fine dining,

skiing, oration

Interests: World War II,

spirits, fast cars

Friends

Patrick has 314 friends

including:

Jim W.

David C.

Mary V.

Jeff D.

Shalini L.

… see more …

Patrick W.

Patrick W. said:

Just installed a gadget in my Acura to

save $ on car insurance… will need the $

for the third home I’m looking at in FL!

Mary V. commented: Your crazy.

(This example is completely hypothetical and

intended for illustrative purposes only.)

Data uniquely suited for LTV

52

Source: ISO Social Media

Insurer Focus Group

(December 2014)

Model-ready social media data

53

Engage with insurer

on social media?

Yes No

Positive Neutral Negative Posts on social

review sites?

No Yes

Mostly

negative

reviews

Mostly

positive

reviews

Reviewed on

commerce

sites?

Yes No

Mostly

positive

reviews

Mostly

negative

reviews

What are

hobbies and

interests?

Active

lifestyle Sedentary

lifestyle

Hypothetical machine

learning example

e(d) = 18 months e(d) = 96 months



e(d) = 44 months

Is the ‘next trillion dollar industry’ waiting for P&C to get well soon?

• Obese 75+% more likely to die in crash car

• Diabetics10-20% more likely to crash car

• 2-3% of crashes involve drowsy driving

• Poor eyesight linked to ~60% of car crashes

• 50+% of work accidents due to drowsiness

• Stress-related workplace claims ~2x costlier

54

Can P&C insurers make a

positive difference? Sources:

Geggel (New York Times 1/28/2013), “Precautions Urged for Drivers with Diabetes”

Melamed and Oksenberg, “Excessive Daytime Sleepiness and Risk of Occupational Injuries in Non-Shift Daytime Workers”

Maine Employers Mutual Insurance Company and David Lee, “Managing Employee Stress and Safety”

National Highway Transportation Safety Institute, “Traffic Safety Facts: Drowsy Driving” (March 2011)

Rice and Zhu (Emergency Medical Journal 1/21/2013), “Driver Obesity and the Risk of Fatal Injury During Traffic Collisions”

Vision Impact Institute, “Ten Things You Need to Know: Concise Facts on Vision Economics”

The little source of transformative data

55

Small takeways…

• Understand the value proposition

• Confirm the data matches the problem

• Clean, timely data required to compete

• Machine learning increasing in prominence

• Policyholders expect value for their big data

56

#thanks

No part of this presentation may be copied or redistributed without the

prior written consent of ISO. This material was used exclusively as an

exhibit to an oral presentation. It may not be, nor should it be relied

upon as reflecting, a complete record of the discussion..


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