Analytics: Emerging Trends and Data Sources...Roof Pitch/Area/ Line Measurements X Roof Material...
Transcript of Analytics: Emerging Trends and Data Sources...Roof Pitch/Area/ Line Measurements X Roof Material...
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Analytics: Emerging
Trends and Data Sources
1 © Insurance Services Office, Inc. 2016
ISO User Conference Chicago, IL – September 13, 2016 #ISO2016UC
© 2016 Insurance Services Office, Inc. All rights reserved. Confidential and Proprietary.
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
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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).
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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
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Environmental Module example
5
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Hundreds of times more precise
6
Territories: 1 ZIP Codes: 34 Block Groups: 669
Example: Milwaukee, Wisconsin, Geographic Area
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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.
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Multi-sourcing example: UBI
8
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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)
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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
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Homeowners: from the inside out
11
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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
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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*
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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
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Homeowners telematics: data possibilities
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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
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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)
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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
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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
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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
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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
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Inclusion through data
20
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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
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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
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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
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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
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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
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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
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Sample analysis stage: keyword ID
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Over 150k original tweets
mentioning selected insurers
Testi-
monial
~10k
Ads/
promos
~75k
Jobs
chatter
~10k
News
stories
~15k
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Sample analysis stage: attribute creation
• Sites/types (15+)
•Connections/followers
• Influence
•Hobbies/interests
• Life changes
• Sentiment
28
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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
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Sample analysis stage: validation
30
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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
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Taking care of business
32
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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
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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.
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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
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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).
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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).
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Potential businessowners data collection technologies
•WiFi
• Smart Badges
•Video
•Cell Phone
•Radar
• Traffic cables
•Cash Registers
•Beacon
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Making real-time business data easy, actionable, private
39
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Symptoms and causes
40
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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
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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
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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
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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
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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.
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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
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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
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Thank You
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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
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