Consider that Florida has the potential for $195 MM of the mispriced risk identified due to a lack of hyper-accurate information. To write competitive premiums and maximize profit, insurers know that it’s critical to improve underwriting accuracy, but are often challenged with the quality and scope of the data that makes this possible. This on-demand webinar walks insurers through the data sources necessary to help boost underwriting proficiency and differentiate them from their competition. Specifically, insurers will learn how hyper-accurate location data, publicly available and third-party-sourced data can be combined, standardized and validated with a unique and persistent identifier, enabling a straightforward approach to claims, CAT management and profitable underwriting. Watch this on-demand webinar to find out how to leverage granular, accurate, and predictive risk variables to mitigate risk across your commercial and home property books of business. Register today and learn: -Strategies to easily organize and enrich your data from a location perspective - How highly precise geocoding impacts pricing - How to utilize AI & ML to enable straight through underwriting processing - Augment, enrich, and connect 3rd party data across your business to make better underwriting decisions for properties impacted by flood, hail, hurricane, and fire

1. PINPOINTINGTHE ISSUE:
Why Hyper-Accurate Location Data Can’t Be
Overlooked in Insurance
Jay Gentry, Managing Director
Mike Reilly, FS Insurance Underwriting

2. The global leader in data integrity
Trust your data. Build your possibilities.
Our data integrity software and data enrichment products
deliver accuracy and consistency to power confident
business decisions.
Brands you trust, trust us
Data leaders partner with us
of the Fortune 100
90
Customers in more than
100
2,000
employees
customers
12,000
countries
Leader in Gartner Magic Quadrant
25 of top 25 P&C Carriers
200+ P&C carrier clients

3. Why Hyper-Accurate Location Data Can’t Be
Overlooked in Insurance
Jay Gentry/Managing Director

4. Copyright © 2020 Accenture All rights reserved.

5. Copyright © 2020 Accenture All rights reserved.

6. Copyright © 2020 Accenture All rights reserved.

7. Copyright © 2020 Accenture All rights reserved.

8. 94%
No change
2%
Overpriced
4%
Underpriced
6.7M Homeowner’s
Policies Re-priced

9. Average Change
in Premium for
Affected Policies19%
Number of
Policies with
change of >40%44K
Average per
policy impact
across ALL
policies
$22
$148M
Total Pricing Error in
Florida

10. CASE STUDY – ACCURATE DATA
Challenges
• Need to improve location accuracy of insured properties/buildings (poor location on 50% of
address book) to increase profitability and reduce loss dollars.
• Need for faster and more frequent location-based information updates
• Facilitate more real-time underwriting and pricing
• Increase new business close ratio
Solution
• Precisely Data and Location Intelligence Solution
Benefits
• Payback: < 17 months
• IRR: 216%
• NPV: $7.7M

11. PRECISELY DATA
PORTFOLIO
FOR INSURANCE
17
A single view of risk makes a difference.
Understand as much as possible about every location.
Gain actionable insights to make better businessdecisions.
Add precision to risk analysis and modeling and turn
information into action.
Improve pricing efficiency, risk management and more.
Improve the customer experience with greater data
and analytics.
Drive bottom-line business objectives across
the organization.

12. A LOCATION’SFINGERPRINT
Precisely Key
Uniquely and
permanently
identifies every
location in the US
Ensure data integrity
Make every address location referenceableby
the same ID to easily reflect:
ZIP Code™ changes
City realignments
County changes
Ownership changes
Etc.
Link between datasets
across companies
Ensure common understanding and
consistency of information between MLD users
• Link using the pbKey
• Maintain privacy – Precisely Key
contains no personally identifiable
information
• Facilitate the transfer for databetween:
• Insurers and Reinsurers
• Title companies and mortgagecompanies
• And many more
• Connect instantly to geo-enrichmentdata
sets like:
• Demographics
• Risk data
• Building Footprints
• PropertyAttributes
• Aerial Imagery
Link between systems
Exchange the Precisely Key instead address
details that could be hard to match
• Avoid creating duplicates
• Reduce need for regular cleanupprojects
• e.g., between an ERP and CRM system

13. 207M
Locations
Mass
Movement
Crime
Earthquake
Fire
Station
Flood
Address
Fabric
Shoreline
Weather
Property
Attributes
Premium
Tax
Wildfire
Street
Network
Points of
Interest
1000+
Unique
Fields
Boundaries:
Parcels &
Building
Footprints
Business
Names
Precisely Offers many ways to Mitigate Risk
and Improve Pricing efficiency

14. Copyright © 2020 Accenture All rights reserved.
CURRENT AND MORE ACCURATEDATASETS TO DELIVER TODAY’SMUST-
HAVEINSIGHTS.
19
Detect risk that
others miss.
• Understand risk
associated with
natural hazards
• Pinpoint pockets
of opportunity
Provide accurate,
competitive pricing.
• Price based on
a more
accurate risk
assessment
• Manage state
filing
requirements
Visualize total
portfolio exposure.
• Enhance
financial
risk models
• Determine PML
Understand which
products consumers
need.
• Improve product
design and
pricing

15. DigIn: The Case for
Location MDM in Insurance
Michael F. Reilly, FS InsuranceUnderwriting
September 2020
Copyright © 2020 Accenture. All rights reserved.

16. There are multiple reasons that carriers
are rethinking their property UW
solutions…
• Underwriters have to go to and enter
data in multiple places and multiple
models
• There are often conflicting or
competing views of location data
• Solutions are overly manual leading to
errors, rework, and costly UW leakage
• Lack of precision of UW data or ability
to accurately aggregate risks is leading
to additional exposures and losses at a
time when hazards are increasing
• Policy solutions can’t process the level
of data needed or desired to be
managed for property insurance
• Inability to effectively share location
information across the enterprise.
STATE OF PROPERTYUW
Why is this
so hard?
2
1
…And the promise and value of a solution are
huge if we can achieve 4 simple goals:
Single Golden Record of Location data independent
of policies
Improve efficiencies in prospecting, risk selection,
pricing, and claims
3 Integration with business applications
Reduce manual intervention and iteration with
brokers
4

17. PROPERTYUNDERWRITINGISDIFFICULTWITHOUTACCURATELOCATIONDATA.
Copyright © 2020 Accenture. All rights reserved.
THEREAREMULTIPLEDATAMANAGEMENTCHALLENGES.
• Location data is stored at a policy level
• Underwriters enter data in multiple places and in multiple models. This perpetuates
conflicting/competing views of location data across data sources
• Specific and granular details on location including risks (flood, crime, fire, earthquake,
hurricane, tornadoes, etc.), proximity to other high risk locations, tenant history etc. are not
integrated and often not readily available
• Policy solutions are missing the right data to manage property insurance. Lack of precision, risk
aggregation is increasing exposures and loses
• Global addresses do not follow a common address standard or have a certifiable regional
authority and may not be at the right level
• No single source of truth for the enterprise, gaps in data from ownership and accounts are
precipitating challenges in sharing and trusting l
• Manual interventions are leading to errors, rework, and costly UW leakage
3
To solve this carriers need to move from a point to point approach for location to an
MDM approach
Why is this
so hard?

18. CREATINGANMDMCAPABILITYFORLOCATIONFOCUSESON5 KEYAREAS
Copyright © 2020 Accenture. All rights reserved. 4
1. Design a Location Master Repository designed specifically for Location data
3. Enable a Flexible Architecture, Data Model, and Workflow to stand up the repository
2. Develop or use a specific location key and heirarcy you can apply to all data
5. Provision Upstream and Downstream systems with a Single Version of the Truth
4. Operationalize Data Governance ownership and stewardship with policies and standards for
manual and automated interventions

19. Copyright © 2020 Accenture. All rights reserved.
• Data Nodal Structure
• Location based hierarchy of parcel, building/asset,
floors/participants
• Standardized Data Key
• Handle static and dynamic information
• Can support data roll-ups and extractions across the
hierarchy
• Can store data at different hierarchical elements
• Can handle competing truths whererequired
ESTABLISHTHESEKEYCHARACTERISTICSOFLOCATIONDATAREPOSITORY
Managing global property information is critical to property underwriting; yet many companies struggle.
Accenture has been leading the charge in defining insurance property solutions for the future.
Challenges:
• Current
solutions have
failed
• Multi-tenant
• Other Property
• Time based
exposure
• Support for multiple geo spatial analysis
• Ability to ore-run set analytics to improve processing
• Ability to handle mix of geo and non-geospatial
analysis
• Map / Layer visualization capabilities
• Ad hoc location grouping analysis
Challenges:
• Standard
solutions can’t
process
geospatial
aspects
Design Data Structures Specific to Location Utilize Geospatial/Fabric Capabilities
Challenges:
• StandardMDM
won’t work for
Location Data
• Competing
data
• Standardized data key
• Geospatial conforming solution
• Hierarchy of data sources and machine learning
analytics to conform critical data
• Flexible structure that can support competing
information while maintaining a single truth
• Multi-address
• Location
Accuracy
• Importance of time-based data to drive value
• Expandable structure to support non-building property
assets such as large equipment, material stocks, etc.
Treat Location as a Unique Identifier
• Specific designs made for each key user group:
• Underwriters
• UW Management
• Risk Control
• Use of visualizations and machine learning alerts to
help manage data overload
Challenges:
• Standard
solutions can’t
process
geospatial
aspects
Purpose-Built Solution with emphasis on DataQuality
5

20. INTEGRATEWITH3RD PARTYDATAPROVIDERSANDAGGREGATORSTOAUGMENT
ANDENRICHLOCATIONDATAFORACCURACYANDPRECISION
Data Precision Cleansing
Hierarchy, Unique Identifier, Risk Data
Geospatial Data – Mapped to parcel,
building, sub-building
• Drones
• Satellites
• Sensors
• Cell Phone Traffic
• Social Media
• BIM systems
Emerging Data Sets
A MDM architecture supported by people, process, and technology components is a cornerstone in enabling location intelligence
6Copyright © 2020 Accenture. All rights reserved.

21. WHILEANENTERPRISEMDMSHOULDBETHEENDGOAL,THEREAREDIFFERENT
PLACESTOSTARTTHEJOURNEYQUICKLY.
Starting Fast
1. Have an end state
vision and
architecture
2. Choose an area
where improved
location data can
immediately drive
benefit
3. Use the landing
spot to expand to
a division and
then enterprise
solution

22. 8
THANKYOU

23. A location-centric approach to MDM puts
insurers in control.
03.
Operationalize
your addresses
Use the ID to build a
contextual view of a location
for better insight.
02.
A trusted ID
Assign a trusted ID that is
unique and persistent to each
address.
01.
Precision
Geocoding
Achieve the highest level of
address integrity and
positional accuracy.

24. Points of
Interest
Weather Data
Consumer
Data
Property
Attributes
Boundaries
Building
Footprints
Connected Data Portfolio
Location Data Ecosystem Operational Impact
 Better risk assessments
 Greater automation of the
underwriting process
 Improved Valuation
 Improved Scenario Testing
 Sales and Marketing Optimization
PreciselyID

25. THANK YOU
Jay Gentry – Managing Director-
Insurance Practice
jay.gentry@precisely.com