Analytics: Emerging Trends and Data Sources...Roof Pitch/Area/ Line Measurements X Roof Material...

© 2016 Insurance Services Office, Inc. All rights reserved. Confidential and Proprietary.

Analytics: Emerging

Trends and Data Sources

1 © Insurance Services Office, Inc. 2016

ISO User Conference Chicago, IL – September 13, 2016 #ISO2016UC


Agenda

• State of analytics in P&C – Prevalence of predictive modeling

– Case studies: ISO Risk Analyzer and UBI

• Emergent data source case studies – Understanding risk inside and out

– Using novel data to expand inclusion

– Leveraging policyholder technologies

– Discerning the symptom from the cause

2


State of analytics in PC

•Most PL carriers use modeling for pricing and

risk selection (97% auto, 73% home)

•Becoming the norm in CL as well (50-60%)

• Increased alternative uses of modeling – fraud

– triage

– targeting

– report orders

•75% in EU expect to double investment in

new data sources over next three years for PL

3

Sources: Willis Towers Watson 2015 Modeling Survey (North

America), ISO/Earnix 2015 External Data and Usage Suvey (UK).

https://www.towerswatson.com/en-US/Insights/IC-Types/Survey-Research-Results/2015/02/infographic-how-are-pc-insurers-using-predictive-modeling-for-competitive-advantage

http://www.verisk.com/survey/new-survey-explores-how-u-k-insurers-are-acquiring-and-using-external-modeling-data.html

© 2016 Insurance Services Office, Inc. All rights reserved. Confidential and Proprietary. 4

Environmental Module

Personal

Auto

Commercial

Auto

Homeowners

Business-

owners

By-Peril Rating Factors Module

Homeowners Business-

owners

Vehicle Module

Personal

Auto

Liability and

Physical

Damage

Commercial

Auto

PPT and TTT In Development

Homeowners Building Characteristics Module

Personal Auto Driver History Module


Environmental Module example

5


Hundreds of times more precise

6

Territories: 1 ZIP Codes: 34 Block Groups: 669

Example: Milwaukee, Wisconsin, Geographic Area


Commercial auto vehicle score concept

7

Truck A Truck B

Weight Class Medium Medium

Age 5 years 5 years

Engine 8.9L, 350hp 10.8L, 435hp

Drive wheels 4 8

Fuel Fudge drizzle Diesel

Today: Broad

groups don’t

differentiate

well enough

Should these two trucks receive the same treatment?

Tomorrow:

differentiate

using detailed

vehicle specs

If you didn’t answer yes, then you agree a next generation solution is required.


Multi-sourcing example: UBI

8


Making sense of UBI data

• Time of day (telematics data feed)

• Speed vehicle traveling (telematics data feed)

• Visibility and traction (weather database)

• Number of lanes (road atlas database) • Speed vehicle supposed to be traveling (traffic database)

9


UBI loss ratio ‘lift chart’

Analysis performed using same vehicles used to train model, but separate

period of 90 driving days to produce estimates. Chart suggests ‘All other things

being equal,’ model identifies one in five that are >10x as risky. 10

© 2016 Insurance Services Office, Inc. All rights reserved. Confidential and Proprietary. © 2016 Insurance Services Office, Inc. All rights reserved. Confidential and Proprietary.

Homeowners: from the inside out

11


From connected cars to connected homes

12

Connected home

(kɘ nekt’ed hōm)

n. A home equipped with electronic devices, such as sensors, appliances, and lighting

and heating applications, that are tied to the Internet and controlled remotely via interfaces such as

phones or computers

© Insurance Services Office, Inc. 2015


0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Automate/regulate

home entertainment

Automate/regulate

home entertainment

Energy management

Energy management

Personal/family

security/monitoring

Personal/family

security/monitoring

Top reasons for using a smart-home system: Security remains #1 but entertainment is surging*

13

Source: Icontrol Networks, 2014 and 2015 State of the Smart Home

*Survey sample is evenly divided between males and females; limited to ages 25+ and homeowners with household income of $50K+ or renters with household income of $40K+

2015

2014

2015

2014

2015

2014

Personal/family security and monitoring

Energy management

Automate/regulate home

entertainment

© Insurance Services Office, Inc. 2015


Homeowners telematics: data possibilities

14

Usage • Patterns of energy and water consumption

• Water running when no occupants are home

• Which rooms are used, when, and for how long?

Occupants • Occupants: number, frequency of access

• Number of smokers; frequency and time of day of smoking

• Number of connected devices

Contents • Movement of contents in and out of the house

• Major appliance location

• Sprinkler system detection

Residence • Roof age and condition; material; weight load

• Wind speed and barometric pressure

• Gas leak detection


US carriers rating by peril have thrived

34%

66%

Five years

later…

28%

72%

Initially

Non-By-

Peril

Insurers

Loss Ratio

77%

25 By-Peril

Insurers’

Loss Ratio

69%

Estimated

Market

Share DWP

(A.M. Best)


Exposure to perils varies by structure

•Construction style and materials

•Roof age and materials

• Square footage and lot size

•# of bedrooms and # of bathrooms

•Heating and cooling systems

•Garage and basement

16

© 2016 Insurance Services Office, Inc. All rights reserved. Confidential and Proprietary. 17

Insurer Roof Age vs. Modeled Roof Age

Insurers (red bars) have the majority of their roof age mix under 17 years old.

Verisk’s Roof Age (blue bars) show a smaller mix of roofs under 17 years old.

Insurers (red bars) usually have a small mix of roofs over 17 years old.

Verisk’s Roof Age (blue bars) shows most roofs are over 17 years old.

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30+

Roof Age Comparison

Insurers Verisk Roof Agex-axis: roof age

y-axis: address mix


Building characteristics example

18

Both houses are in the same postcode, same ITV of $300k, same year of

construction and in the same public protection classification

3,000 sq foot 4 bed, 3.5 bath

colonial brick exterior, attached 2

car garage

2,500 sq foot 3 bed, 2.0 bath ranch

wood siding, 2 car carport, pool

44% more

prone to water

damage

35% more

prone to liability

2x

more prone to

hail

damage


Sample structure-specific data

Feature

Aerial Imagery X

Roof Diagram / Sketch X

Roof InSight Report X

Roof Pitch/Area/

Line Measurements X

Roof Material Analysis X

Roof Vent Count X

Roof HVAC Count X

Roof Gutter Length X

Exterior Wall Diagrams X

Exterior Wall Material

Analysis X

Exterior Door Count and

Size X

Garage Door Count and

Size X

Exterior Window Count

and Size X

Exterior HVAC Count X

Number of Floors X

Porch/Patio/Deck Diagram X

Pergola Diagram X

Shed/Barn Diagram X

Tree Canopy Diagram X

Pool/Sports Court Diagram X

Skylight Count and Size X

Chimney Count X

Real-Time Availability X

Application to

Underwriting,

Real Estate, and Other

Areas

X

19


Inclusion through data

20


New data abound in growth markets

% of adult population with social media vs. financial account

Sources: World Bank (2014) and We Are Social (1/2015).

21

http://databank.worldbank.org/data/reports.aspx?source=1228

http://wearesocial.net/blog/2015/01/digital-social-mobile-worldwide-2015/


Types of social media

22

Media

Sharing

Networks

Thumb, Foodspotting,

Fitocracy, Flipora,

Yammer, Tinder

Discussion Forums

Book-

marking

Sites

Social Publishing

Online

Reviews

Interest-

based

Networks

Personal

Networks

Facebook, Google+,

LinkedIn, Twitter,

Ello, Tsu

YouTube, Vimeo,

Snapchat, Vine, Flickr,

Instagram, Podcasts AirBnb, Uber, Yelp,

Zomato, Glassdoor,

Tripadvisor

Quora, Digg, Yahoo

Answers, WikiAnswers,

StackExchange

Medium, Tumblr,

Reddit, WordPress,

Blogger Stumbleupon, Pinterest,

Flipboard, WeHeartIt

Polyvore, eBay, Etsy,

Amazon Marketplace

e-C

om

merc

e

Source of categories:

Hootsuite

http://blog.hootsuite.com/types-of-social-media/


Potential P&C uses of social media data

23

How soon will micro-blogs be useful for each of the following areas?

Source: August 2014 ISO Survey, n = 44

Growth rate

Pe

rce

nta

ge

of

Re

spo

nd

en

ts


Sample social media sites

24

Which of the following platforms would be useful for P&C risk assessment?

Source: August 2014 ISO Survey, n = 44

Pe

rce

nta

ge

of

resp

on

de

nts

in

dic

atin

g u

sefu

lne

ss

Approximate launch date of platform


Potential web communications

25

May be originated

by Insurer(s)

• Advertising

• Resources

• Job postings

• Address feedback

Insurer(s) may be

mentioned

• Testimonial

• Buzz

• Employee review

• News coverage

• Synonyms

Potentially relevant,

but may not be mentioned

• Fraud-related

behavior

• Risk-related behavior

• Shopping-related

behavior


Sample analysis: auto prediction model

Sample competencies:

•Archive public data

• Identify testimonials (n=1000)

• Search web presences

•Resolve to subject

•Create risk attributes

• Train claim prediction algorithm

26


Sample analysis stage: keyword ID

27

Over 150k original tweets

mentioning selected insurers

Testi-

monial

~10k

Ads/

promos

~75k

Jobs

chatter

~10k

News

stories

~15k


Sample analysis stage: attribute creation

• Sites/types (15+)

•Connections/followers

• Influence

•Hobbies/interests

• Life changes

• Sentiment

28


Sample analysis stage: decision tree

29

No

(Freq. 16%)

Yes

(Freq. 24%)

Full sample

(Frequency 19%)

Site A Profile

< -0.6882

(Freq. 42%)

Site B Connections

n/a or >= 0.6882

(Freq. 15%)

< -0.6378

(Freq. 50%)

n/a or >= 0.6882

(Freq. 22%)

< -1.5206

(Freq. 67%)

n/a or >= -1.5206

(Freq. 21%)

< -0.2554

(Freq. 75%)

n/a or >= -0.2554

(Freq. 31%)

Influence metric Site C Connections

< -0.4397

(Freq. 55%)

n/a or >= -0.4397

(Freq. 11%)

< -1.9326

(Freq. 55%)

n/a or >= 1.9326

(Freq. 20%)

Interests metric

Preliminary decision tree based on1,000

subject experiment


Sample analysis stage: validation

30


Potential benefits of using social media data in P&C

• Considers unique dimensions of risk – Life events and achievements – Connections and social activities – Sentiment and reviews

• Economical and timely

– Accessible via open APIs – Constantly refreshing

• Originates with policyholders

– Curated content – Publicly available data

• Improves access for underserved

– Potential discounts for traditional “no hits” – Growing use in lending space

31


Taking care of business

32


Businessowners state of the union

• Fairly stable market with established players

• Individualized underwriting becoming rarer

•More online/direct distribution options

• Larger and more complex risks on BOPs

•New risk factors such as cyber and drones


Restaurants and small retail indicate above average loss ratios

For more information, see article “Businessowners Policy Pricing – Yesterday, Today, and

Tomorrow” (Su Wash) in ISO Between the Lines.

http://www.verisk.com/between-the-lines/october-2015.html


Traditional view of BOP risk

• Construction – Frame vs. fire-resistive – Age of building

• Occupancy – Class group – Rate number – IRPM

• Protection – PPC – Sprinkering

• Exposure – Limit of insurance – Payroll, square footage – Territory

35


Emerging hazards may require site-specific intelligence

36

0

0.25

0.5

0.75

1

Potential hazards related to restaurants and retail

Source: ISO Businessowners Survey (September 2015).


If you could collect anything …

37

0

0.15

0.3

0.45

0.6

Potentially predictive data elements

Source: ISO Businessowners Survey (September 2015).


Potential businessowners data collection technologies

•WiFi

• Smart Badges

•Video

•Cell Phone

•Radar

• Traffic cables

•Cash Registers

•Beacon


Making real-time business data easy, actionable, private

39


Symptoms and causes

40


Wearables and insurance

41

“3% of insurers are already using wearable devices and another 3% are

experimenting with the new technology, while 22% are in the process of

developing a strategy for using them.” - Strategy Meets Action Research - 2014


Perspectives on why accidents happen

• 5x higher crash risk among truck drivers with untreated sleep apnea – AASM 2016

• Over 1 in 5 fatal accidents involves a drowsy driver – AAA 2014

• Americans with BMI over 35 are over 50% more prone to fatal accidents – FARS 2010

• Workers reporting ‘high stress’ have medical costs over 50% higher – AOCEM 1998

• Refinements to eyesight testing could save tens of millions – RSA 2012

42

http://www.aasmnet.org/articles.aspx?id=6186



https://www.aaafoundation.org/drowsy-driving

http://www.edmunds.com/car-safety/obese-drivers-face-higher-risks-in-cars.html

https://www.sciencedaily.com/releases/1998/10/981019073351.htm



http://www.roadsafetygb.org.uk/news/2487.html




How are some insurers responding?

• Life insurer provides free fitness trackers

•Dental insurer offers connected toothbrush

•Auto insurers initiate patent process for

biometric technology

•CL insurer invests in workplace safety startup

43


Sample fitness tracker data specification

44

Device / user

• Type

• Sync

• Charge

• Alarm settings

• Friends *

• Badges *

Activity

• Calories

• Distance

• Elevation

• Exertion (time at

different levels)

• Floors

• Steps

Self-Reported

• Food

• Water

• Weight

Sleep

• Count awake

• Count reckless

• End time

• Start time

• Time asleep

• Time awake

• Time reckless

• Time to fall asleep

Heart Rate

• Max

• Min

• Resting Rate


Behavioral risk factor analysis Condition BI PD Collision Comprehensive

Good Mental Health - - - +

Bad Mental Health

Heart Attack

Angina/Coronary -

Stroke +

Current Asthma -

Former Asthma + + +

Never Asthma -

Obese +

Over Weight

Average BMI - -

Diabetes + +

Prediabetes

Regular Smoker

Smoker - - +

Former Smoker

Heavy Drinker

45

Source: Analysis of ISO Personal Automobile Statistical Plan (PASP) and

county-level CDC Behavioral Risk Factor Surveillance System data.


Data collection possibilities*

46

Device / user

• Type

• Sync

• Charge

• Alarm settings

• Friends *

• Badges *

Activity

• Calories

• Distance

• Elevation

• Exertion (time at

different levels)

• Floors

• Steps

Sleep

• Count awake

• Count reckless

• End time

• Start time

• Time asleep

• Time awake

• Time reckless

• Time to fall asleep

Heart Rate

• Max

• Min

• Resting Rate

* Subject to wearer[user’s] consent

© 2016 Insurance Services Office, Inc. All rights reserved. Confidential and Proprietary. © 2016 Insurance Services Office, Inc. All rights reserved. Confidential and Proprietary. 47

Parting thoughts •Most insurers now using predictive modeling and finding value

•Connected home and aerial imagery enable more holistic

understanding of property risk

•Social media and other new data sources have potential to

improve inclusion domestically and globally

•Connected businesses are already collecting data that could be used collaboratively with insurers

•Wearable technologies are furthering industry movement from

symptom analysis to causal analysis


Thank You

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..

#ISO2016UC

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