The document discusses how The Hanover Insurance Group is advancing business value through predictive analytics and data science. It outlines top analytics trends like product innovation, improving the customer claims experience through behavioral analytics, and improving underwriting profitability with advanced analytics. The presentation also provides examples of how Hanover is using claims analytics for fraud prevention, claims triaging, and leveraging analytics to power faster responses to catastrophic events.

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Srinivasan Sankar
The Hanover Insurance Group
Advancing Business Value Through Predictive Analytics and Data Science
Boston CDO Executive Summit - June 26, 2018 - Hyatt Regency Boston

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Business Overview
• National P&C Insurance company founded in 1852 and headquartered in Worcester, Massachusetts
• Traded on NYSE under ticker symbol THG
• One of the largest insurance companies in the U.S.
• Approximately 5,000 employees across the U.S. & U.K.
• Named by Forbes magazine as one of “America’s 50 Most Trustworthy Financial Companies”
• Partners with a select group of approximately 2,200 independent agencies nationwide for product
distribution
• Underwrites a wide range of personal and commercial lines for businesses, individuals, and families
• Integrated several strategic U.S. and international acquisitions in recent years that dramatically
expanded the company’s product portfolio and geographic footprint
• $5B+ in revenues
• Excellent rating from the major ratings agencies

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Disclaimer
This presentation is for general informational and discussion purposes only.
The opinions expressed in this presentation are those of the presenter and not
necessarily those of The Hanover Insurance Group (“Hanover”). Neither
Hanover nor the presenter guarantees the accuracy or reliability of the
information provided herein. No representation or warranty, express or
implied, is provided by the presenter or Hanover in relation to the fairness,
accuracy, correctness, completeness or reliability of the information, opinions
or conclusions expressed herein. The information contained in this
presentation is subject to change without notice. This presentation does not,
and is not designed to, provide legal advice and meeting participants should
consult with an attorney concerning the use of data to ensure all legal and
regulatory requirements are satisfied.

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Agenda
• Top Five Analytics Trends
• Shift from traditional analytics to modern analytics
• Old World vs. New World
• Claims Analytics – Fraud prevention
• Claims Triaging, Claims Mitigation
• Analytics powering CAT response
• Questions and Comments

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Innovation in P&C Insurance Top Five Analytics Trends
1. Product Innovation on the Rise and Product Analytics Are Required
– Launching new products based upon emerging needs of digital and millennial
customers
2. Adopting New Types of Analytics to Improve the Customer Claims
Experience
3. Building Behavioral Analytics Using IoT Data and Applying It Throughout
The Enterprise
– Location, Time of Day, Safety Indicators, Driving Patterns
4. Improving Underwriting Profitability Through New Data and Advanced
Analytics
– Embedded Analytics Using Big Data and Artificial Intelligence
– Automated Underwriting
5. Fraud Analytics

6. 6© Gartner Research, Inc.
By 2021, 75% of prebuilt reports will be replaced with or augmented by automated insights delivered
on a "most needed" basis.
Smart Data Discovery
Visual Exploration
Analytic Dashboards
Storytelling/Narrative
Traditional analytics platforms
are designed to support
modular development of IT-
produced analytic content,
specialized tools and skills,
and significant upfront data
modeling, coupled with a
predefined metadata layer, is
required to access their
analytic capabilities.
A modern analytics platform
supports IT-enabled analytic
content development. It is
defined by a self-contained
architecture that enables
nontechnical users to
autonomously execute full-
spectrum analytic workflows
from data access, ingestion and
preparation to interactive
analysis, and the collaborative
sharing of insights.
Traditional Analytics to Modern Analytics - A Strategic Outlook

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How the shift happens – some examples
Old World New World
• Product recommendation
from advisor experience
• Risk group classified using
static data, e.g. age and
medical history
• Labor-intensive claim
processes take months to
finish
• Customer churn analysis
after the fact
• Many loopholes in insurance
fraud detection
• Product recommendation from
deep analytics of customer data
• Risk group classified using broad
spectrum of data, e.g. social
media, click streams and web
analytics
• Claims processed faster using
automated image classification
• Customer churns predicted and
intercepted in real time
• Insurance frauds more
accurately detected from
customer 360º analysis
• Increased revenue

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Claims Analytics – Fraud Prevention
Loss costs are the key components of insurer results. Fraud prevention is $36B in the bottom-line
Loss $6k
Expense
$3k
Profit
$1k
Premium
$10k
Identifying
Fraud
here
Will
increase the
bottom-line
here
Claims
Fraud
Model
Segment and
Prioritize
Claims
Identify Fraud
Potential
Rare
event
modeling
Algorithms
SMOTE* (to
handle skewed
Data)
Cost
Savings
• 10% of Total
Loss goes into
Fraud
• $600 B P&C
industry
• Loss Ratio @
60% avg.
• Total loss =
$360B
• 10% = $36B
*Synthetic Minority Over-sampling Technique
Fraud at a glance

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• Long tail claims
(bodily injury
claim severity
escalation)
• Predictions
based on
historical data
• Surgery
predictions
• Attorney
involvement
prediction
• Input to Claim
mitigation in
the business
process
Claims details
EDW
Unstructured claim
notes
Claims System
Machine Learning
Deep Learning
15+ Algorithms for Fraud
Detection
Modeling and Analytics
Real-time
Scores
Green = Procced further in the Claim Process
Yellow = Proceed with Caution
Red = Needs further investigation
Fraud prevention, Real-time Scoring and Predictive Analytics
Claims Triaging, Claims Mitigation

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Analytics powering faster CAT response
• Storms tracking - keeping watch on where and when landfall expected
• Potential coverage impacts (Flood, Building, Marine, Auto, etc.)
• Help Claims department in determining how to act for each state so they
could mobilize and deploy staff

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http://www.linkedin.com/in/srinisankar
https://twitter.com/srinisankar