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Connecting Home/Building, Life, and Car ...
The Importance of Insurance Risk Monitoring
Cindy Maike -- VP Industry Solutions and GM Insurance & Healthcare
October 16, 2018

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Our Daily Lives and
Today’s Businesses
Are Connected by Data

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Insurance Industry Data Sources in the Connected Digital World
Catastrophic
Event Data
Customer
Onboarding Data
Seismic
Data
Biometrics
Data
Usage-Based
Driver Data
Cyber Threat
Metadata
Drones &
Aerial Imagery
Claims Docs,
Notes & Diaries
Weather &
Environment
News Feeds
Policy
Histories
Photos
Telematics
Connected
Devices/IoT/
Sensors
Chat Bots
Call
Transcripts
RISK & UNDERWRITING
ANALYSIS
USAGE-BASED
INSURANCE
CLAIMS
ANALYTICS
NEW PRODUCT
DEVELOPMENT
CYBER RISK
ANALYTICS
CRM

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Insurance Big Data Analytic Drivers & Opportunity Areas
Data
Granularity
New
Data
Sources
Secondary Data
Access
Real-tim
e
Risk Analysis
02
Secondary Data Access
• Geocoding
• Link analysis
• Behavioral data
• Environmental
• Social/economic data
03
• New predictive variables
• Open Data / Open City
• Access to telematics/wearable
sensor
• Digital / Web data
• Better access to unstructured
data
• Historical data
• Text analytics
• Access to larger data sets
04
• Integration of risk analytical
models with underwriting process
• Usage of streaming event
information
• Catastrophe modeling and impact
analysis
01
New Data Sources
Data Granularity
Real-time Risk Analysis

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Risk Listening…Focus on Monitoring and Prevention
A N Y D ATA
Existing and new datasets
B U S I N E S S VA L U E
Understand Data ‘Richness”
A N A LY T I C A L VA L U E
Method of Analysis and Use
DATA REPOSITORIES
Underwriting
EDW
Claims
Product
Commission &
Billing
Finance
TRADITIONAL SOURCES
POLICY
RECORDS
CRM RECORDS
RISK & CLAIM
MODELS
MORTALITY
TABLES
APPLICATION
DOCUMENTS
MEDICAL BILLS CLAIMS DATA
PRIOR LOSS &
VENDOR REPTS
EMERGING & NON-TRADITIONAL SOURCES
TELEMATICS,
MOBILE, DRONE
SOCIAL MEDIA
CLICKSTREAM
WEB LOGS
MARKETING
RESEARCH
UNDERWRITING
NOTES
EMAILS OPEN DATA SETS TRANSCRIPTIONS
Ease of Access
Real-time
Impact of the Data
(Questions Answered)
Transformative/New Business Models
All of the Above
Reporting
Monitoring
Predictive
Discovery/
Learning
Preventive

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Challenge Is Many Organizations Struggle to Understand the
Difference Between Big Data And Big BI
Big Business Intelligence vs. Big Data
Big BI
DATA Big Data
§ Same analysis as before, just
more data
§ Batch or warehouse-type
processing
§ Informative, but not really
actionable
§ Joining data sets never before
joined, asking questions never
before asked
§ Real-time or near real-time,
leading to
predictive/persuasive/preventive
§ Ability to extract the value of
data and quickly translate into
action – ”Data Agility”
§ Action-oriented, driving “Action
at the Speed of Insight”
Many apply traditional reporting methods to Big Data & Advanced
Analytics resulting in missed opportunities and limited financial return

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Going forward…How to ’Rethink’
Insurance Data & Analytics (illustration)
BatchReal-time
Datavelocity
Dimensional/Structured Behavioral/Unstructured
Data variety
Semi-structured
Climate / air quality / weather events
Drone image feeds
Social media / Sentiment
Sensors/ Wearables (IoT)
Geo-location
Deposition recording
Notes and diary
Medical bills
Transcriptions
Photos
Investigation
TPA invoices
FNOL intake
Claims triage
Vendor invoices
Forms and
letters
Claim payments
Policy verification
Applications / Submissions
3rd party risk models
Prior loss runs
Customer Profiles
Currently using for analytics
Currently available, but unable to use for analytics
Plan to use in next 6 months
Plan to use in next 12 – 18 months
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Historical Weather-events1
Population data1
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Call center Chat bots2 3

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Examples from
the Challengers
& Innovators

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Traditional “PAYD” “PHYD”/”MHYD”*
Shift from Usage-Based Auto Insurance Programs to Behavioral Rating
Method Adoption
• Vary by geography and locations due to data
availability, cost and regulations
• Companies have struggled with data storage and
analysis due to volume, variety and velocity
• Programs are evolving as technology changes
Data / Analytics Impacts
• Changes in data storage and analysis due to
volume, variety and velocity
• Need to provide additional “context” to the
driving data such as location, weather, road
conditions, accident data
Risk
Proxies
Utilization
Simple
Behaviors
Using
Event
Counters
• Estimated Mileage
• Garage location
• Prior claim history
• Maneuvers
• Anticipation
• Aggression
• Adaptability
• Acceleration
• Braking
• Cornering
• Excess speed
• Approximate location (GPS)• Number of trips
• Time of day
• Mileage
* MHYD = Manage How You Drive

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Telematics Analytics (Machine Learning) to Understand
Context of Driving Conditions
A ”still” view
taken from
the car’s
dashcam.
Through
machine
learning
understanding
the context of
the location
attributes
(orange vs. grey
fields)
Conditions of the
road surface, where
extreme values show
the presence of
speed bumps (as
seen in the picture).
Shows driver
behavior/speed
in this situation
Using geo-
location
Street view to
compare with
the dashcam
view.
In partnership with HDP and HDF
Global Top 30 Multi-line carrier

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Enhancing Telematics Programs…Customer Interaction Providing
Insights for Safer Routes
Enhance Customer
Relationships
Enhance Customer
Relationships
§ Leverage new data sources such as
Data.gov
§ Historical accident prone roadways
& streets
§ Dept of Transportation + driver
behavior
§ Recommend alternate “safer routes”
§ Driver behavior analysis
Vehicle Concierge
Service replaces
“roadside assist
offering”
Data Drives
the Connected
Vehicle
Enhance
Customer
Relationship
Vehicle Concierge
Service replaces
“roadside assist
offering” Loss Expense
Optimization
§ Maintenance reminder
§ Roadside assistance
§ Parking management
§ Travel discounts
§ Service discounts
§ Stolen Vehicle Location
Assistance
Enhance Claim settlement/subrogation
§ Product Liability: Vehicle
manufacturer at fault (e.g. faulty
airbag)
§ Speed claims settlement process
§ Accident investigation (camera,
distance of vehicle)

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Open Source In-Vehicle Infotainment (IVI) – GENIVI® Alliance

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GENIVI® Alliance and Las Vegas Connected Vehicle Pilot
Improve pedestrian safety and traffic flow
Key Pilot Goals
• Increase awareness of pedestrians and bus stop safety
along with improving traffic flow
• Understand in-vehicle messaging of roadway conditions and
impact on driver behavior
• Define a method to collect and utilize data for future
infrastructure planning
• Field deploy an open software standard for vehicle-to-city
communication
• Develop an vehicle-to-city communication approach that
multiple cities can use

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USING IMAGES TO BETTER GUIDE YOUR ACTIONS
(C) 2017 SynerScope

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2 - Satellite
• Situation pre-event
3 - GPS Location
• Drone path (green)
• Water Level Monitors
(black)
• Water Level Crowd
Reporting (orange)
4 - Water Levels
• Showing surge pattern
Post Hurricane Event Analysis…Data Layers Fused
with AI and Advanced Visualization
6 - Aerial Pictures
• Detecting similarity of events
in unstructured data analysis
5 - AI Clustering
• Drones, Helicopters, Planes
(source from state & local
gov’t)
1 - Satellite
• Situation post-event
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In partnership with HDP and HDF
Top 25 US Comm’l & Personal Lines Carrier

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Example of Risk Monitoring/Listening
Insurer wanted on-going views into its bio-med industry customers for
current/future potential liability claims
External data sources were monitored in real-time (news feeds,
content from the customer’s website, stock/financial analysts
commentary) for 50 of top customers for potential liability claims.
• Expanded to 1K customers by end of 2016 as underwriters, product
managers and the claims organization found it very useful for
individual risks.
• Product management and actuarial also are using data to better
understand the risk correlations for the bio-med industry.
• In 2017, expanding and collecting data on 50K customers/target
market.
Outcomes
Approach
Business Challenge
1 2 3
Global P&C: Personal and Commercial
CAPABILITIES: Blending of a new predictive model
with risk monitoring for high-risk attributes.
Expanded insights to a customer market segment

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Traditional Patient Vital Monitoring
In a typical hospital setting, nurses do rounds approximately every 15-
30 minutes and manually monitor patient vital signs, but the patient’s
condition may decline between the periodic visits.
Caregivers often respond to problems reactively, in situations where
arriving earlier may have made a huge difference in the patient’s
outcome.
Example:
Give “patient A”, “drug A” and monitor outcome in patient vital signs (body
temperature, pulse rate, O2 levels, blood pressure, respiration, etc).
Give a similar “patient B” drug B and monitor what happens to that patient.
Over time with multiple identical symptom types/cohorts to find what is the
optimal prescription, dosage, occurrence, and combination yields the best
results for a specific patient.
Patient
Records
Pharmacy
Data
Physician
Notes
Vital Signs
Lab Results
Large Cancer & Medical Research Center

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Now: Real-Time Patient Monitoring with Sensor data
Sensor data is monitored
New wireless sensors capture and
transmit patient vitals at much higher
frequencies, and these measurements are
streamed into a Datalake with
Hortonworks Dataflow
Precision Medicine for Cancer
Treatment
Detailed, frequent readings of
patient vital signs are streamed into
the Datalake and provide the raw
data to build predictive algorithms
for trying to predict the impacts of
various cancer treatment protocols
and drugs.
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Caregivers can use these signals for
real-time alerts to respond more
promptly to unexpected changes..
Real-time alerts to
caregivers
Over time, this data is used for
algorithms which proactively
predict the likelihood of an
emergency even before that could
be detected with a bedside visit.
Predictive / Preventive
• Proactively Predict Events rather than
reactively
• Real-time Alerts
• Capture & Transmit Patient Vitals at
Much Higher Frequencies
Benefits
Managing the
volumes of
patient sensor
data
“Our future is no doubt NiFi and HDF.
We absolutely see all future data flows
and ingestion being done using HDF”.
Large Cancer & Medical Research Center

Where to Start?

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Risk Listening Is One of the Top Innovative Use Cases with Significant
Financial Upside to the Combined RatioNewTypesof
Analytics
New Types of DataExisting Data
Existing
Analytics
• EDW & ETL data & load balancing
• Cost & flexibility
• Building new skill sets
• Legacy IT challenges
• Single-View of Customer showing full 360-
degree profile and history
• Better understand customers to drive
timely, personalized engagement and
informed decisions
• Analyzing submission and claims models
against larger historical data sets
New Historical View
IT Optimization New Data Influencers
• Collecting Sensor/Telematics for Usage Based
Insurance
• Enhanced Claim Severity and Frequency Models
using “new” predictive data
• Customer Sentiment
• Enhanced Loss Control / Prevention Services
• Needs based coverage vs. traditional coverage
New Analytics Applications
• Risk Listening / Large Loss
• Text Analytics and Link Analysis for Claim
Anomaly and Fraud Analysis/Detection
• Enhance Risk Analysis with Related Party
Network Link Analysis
• InsurTech Investment data analysis

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Going Forward….Rethink Insurance
Data & Analytics (illustration)
BatchReal-time
Datavelocity
Dimensional/Structured Behavioral/Unstructured
Data variety
Semi-structured
Climate / air quality / weather events
Drone image feeds
Social media / Sentiment
Sensors/ Wearables (IoT)
Geo-location
Deposition recording
Notes and diary
Medical bills
Transcriptions
Photos
Investigation
TPA invoices
FNOL intake
Claims triage
Vendor invoices
Forms and
letters
Claim payments
Policy verification
Applications / Submissions
3rd party risk models
Prior loss runs
Customer Profiles
Currently using for analytics
Currently available, but unable to use for analytics
Plan to use in next 12 months
Plan to use in next 24 months
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Historical Weather-events1
Population data1
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Call center Chat bots2 3

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Preparing for the Connected World & Risk Listening
§ Track Emerging Tech: All insurers should actively follow those
emerging technologies that will have the highest impact on
their business, such as autonomous vehicles, AI, drones, the IoT,
blockchain, and many others.
§ Follow Smart City Projects: Be aware of the projects that are
underway, especially those in nearby cities or cities where you
have a large customer base.
§ Engage in Initiatives: Where appropriate, join consortiums or
partner with local governments to promote smart city projects,
especially those aimed at reducing risks and improving public
safety.
§ Build Big Data and Analytics Capabilities: In any future scenario
for insurers, having the platforms and expertise to manage,
analyze, and act upon large streams of real-time data will be
essential.
§ Design New Products: Leverage innovation to develop new
products and coverages that address new or increased risks
created by smart cities, or provide insurance in new areas or to
new customer segments.

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Let’s Keep the
Conversation Going…
cmaike@hortonworks.com
@cmaike76
+1 913.484.6000
https://www.linkedin.com/in/cindy-maike/

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Thank You