CEB Towergroup Insurance Webinar: Empower Underwriters with Data, Analytics and Secret Sauce

FINANCIAL SERVICES PRACTICE
CEB TOWERGROUP INSURANCE
Empower Commercial Lines
Underwriters with Data,
Analytics, and “Secret Sauce”
September 24, 2014
Sponsored By

2
© 2014 CEB. All Rights Reserved.
Q&A
Insurance Industry
Trends
Commercial
Insurance Risk
Analytics (CIRA)
ROADMAP FOR THE PRESENTATION

3
Data and Analytics
Imperatives
Business Drivers
& Strategic
Responses
Commercial
Lines Drivers
Insurance Industry Trends

4
INSURANCE BUSINESS, STRATEGIC, AND TECHNOLOGY
PRIORITIES FOR 2014
Source: CEB Analysis
Business Drivers
• Evolving individual sales and service expectations
• Changing distributor business models
• Contentious scope and authority of insurance regulators
• Global dependence on volatile regional economies
• Intensified competition for critical skill sets
Strategic Responses
• Democratize the operationalization of the voice of the
customer
• Build a holistic enterprise-wide data strategy
• Rationalize IT portfolio to align to agile sourcing strategy
• Define standards for favoring agility over precision
• Redefine traditional insurance roles and structures
Top 10 Technology Initiatives for Insurance (2014)
Life & Annuity and Property & Casualty
Create a horizontal
enterprise analytics
and models layer
Intermediate IT and
business cloud
utilization
Facilitate real-time
decisioning with
collaboration technology
Integrate consumer
and distributor portals
with back-end
technology
Leverage increasing
variety of core system
delivery options
Create a device-
agnostic mobile
infrastructure
Top Life & Annuity Technology Initiatives
for Insurance (2014)
Manufacture risk solutions with
integrated actuarial systems
Optimize the value of CRM across
diverse distribution channels
Top Property & Casualty Technology
Initiatives for Insurance (2014)
Integrate big data streams into
day-to-day operations
Expand telematics applications
beyond personal auto

5
Source: IIABA’s 2013 Best Practices Study
Agency Specialization
Percentage of Survey Respondents Reporting an Increase in Specialization, by Revenue
Group, 2010 and 2013
Distributor business
models are shifting and
show an increase in the
specialization of
independent agents and
brokers.
CHANGING DISTRIBUTOR BUSINESS MODELS
25%
44%
51%
56%
64%
68%
39%
56%
64%
72% 73%
80%
<$1.25 million $1.25–2.5 million $2.5–5 million $5–10 million $10–25 million >$25 million
2010 2013

6
n = 1,330
Source: PwC 15th and 16th Annual Global CEO Surveys
Q: How concerned are you about the availability of key skills as a business threat?
Percentage of Respondents, 2011 and 2012
A global scarcity of
skill sets drives
competition amongst
all global industries for
talent, and significantly
impacts insurers’
ability to attract and
retain top talent.
COMPETITION FOR CRITICAL SKILL SETS
INTENSIFIES
13%
10%
33%
31%
39%
41%
15%
17%
2011
2012
Not at all concerned Not very concerned
Somewhat concerned Extremely concerned

7
Source: Insurance Information Institute, Bloomberg, Aon Benfield
Economic Interdependence
US Impact of 2011 Thailand Floods, Illustrative
International financial
markets are now tightly
interconnected, and,
as economies fluctuate,
insurers of all sizes with
insured entities and
supply chain
dependencies spread
across the globe face a
significant risk
management challenge.
GLOBAL DEPENDENCE ON VOLATILE REGIONAL
ECONOMIES
Thailand Floods, 2011
Total Insured Loss
(USD millions):
$15,315
Impact on US Business:
Technology and Auto
Manufacturers & Suppliers
Hewlett Packard: 3.5%+ decline
in 2011 revenue
Ford: $80 million loss
Fluctuations Across Economies
Percent Change in GDP over Corresponding Period of Previous Year, Advanced and
Emerging & Developing Economies, 1998–2012
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Advanced Economies Emerging and Developing Countries
Source: IMF
Decrease in Thailand
manufacturing output
due to factory closures
Approximately 1,007
(billions THB) in economic
losses in manufacturing

8
TECHNOLOGY PRIORITIES FOR PROPERTY AND
CASUALTY INSURERS IN 2014
Source: CEB Analysis
Top 10 Technology Initiatives for Insurance (2014)
Life & Annuity and Property & Casualty
Create a horizontal
enterprise analytics
and models layer
Intermediate IT and
business cloud
utilization
Facilitate real-time
decisioning with
collaboration technology
Integrate consumer
and distributor portals
with back-end
technology
Leverage increasing
variety of core system
delivery options
Create a device-
agnostic mobile
infrastructure
Top Life & Annuity Technology Initiatives
for Insurance (2014)
Manufacture risk solutions with
integrated actuarial systems
Optimize the value of CRM across
diverse distribution channels
Top Property & Casualty Technology
Initiatives for Insurance (2014)
Integrate big data streams into
day-to-day operations
Expand telematics applications
beyond personal auto

9
Data and Analytics
Imperatives
Business Drivers
Commercial
Lines Drivers

10
Combined ratios must
be lower in today’s
depressed
investment environment
to generate risk
appropriate ROEs.
A 100 COMBINED RATIO ISN’T WHAT IT ONCE WAS
Investment Impact on ROEs
Combined Ratio/ROE
*2008 -2014 figures are return on average surplus and exclude mortgage and financial guaranty insurers. 2014:Q1
combined ratio including M&FG insurers is 97.3; 2013 = 96.1; 2012 =103.2, 2011 = 108.1, ROAS = 3.5%.
Source: Insurance Information Institute
100.8
92.7
95.7
101.2
99.5
101.0
106.5
102.4
96.7
97.4
9.6%
12.7%
10.9%
4.3%
7.4%
7.9%
4.7%
6.2%
9.8%
8.2%
0%
2%
4%
6%
8%
10%
12%
14%
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014:Q1
85
90
95
100
105
110
Combined Ratio ROE*

11
While the 1st quarter
2014 marked the 11th
consecutive quarter
with positive pricing,
making up for
significant prior
premium decreases will
not happen at current
rates.
AVERAGE COMMERCIAL RATE CHANGE
All Lines
1st Quarter 2008-1st Quarter 2014
Note: CIAB data cited here are based on a survey. Rate changes earned by individual insurers can and do vary,
potentially substantially
-13.5%
-12.9%
-11.0%
-6.4%
-5.1%
-4.9%
-5.8%
-5.6%
-5.3%
-6.4%
-5.2%
-5.4%
-2.9%
-0.1%
0.9%
2.7%
4.4%
4.3%
3.9%
5.0%
5.2%
4.3%
3.4%
2.1%
1.5%
-16%
-11%
-6%
-1%
4%
9%
1Q08
2Q08
3Q08
4Q08
1Q09
2Q09
3Q09
4Q09
1Q10
2Q10
3Q10
4Q10
1Q11
2Q11
3Q11
4Q11
1Q12
2Q12
3Q12
4Q12
1Q13
2Q13
3Q13
4Q13
1Q14

12
Cyber and reputational
challenges are the most
significant movers in
this year’s
Risk Barometer
rankings, and cyber
crime moved into the
top 10 global business
risks for the first time.
TOP 10 GLOBAL RISKS FOR 2014 ARE COMPLEX
Types of Global Risks
Rank, 2014
10%
10%
12%
14%
15%
19%
21%
24%
33%
43%
Quality deficiencies, serial defects
Theft, fraud, corruption
Cyber crime, IT failures, espionage
Intensified competition
Loss of reputation or brand value (e.g. from social
media)
Market stagnation or decline
Changes in legislation and regulation
Fire, explosion
Natural catastrophes
Business interruption, supply chain risk

13
Data and
Analytics
Imperatives
Business Drivers
Commercial
Lines Drivers

Poll Question 1
To which business areas is your organization
currently applying big data?
1. Pricing and Underwriting
2. Risk
3. Fraud
4. Claims
5. Not Doing it yet

15
Few organizations
translate increased
investments in
information
management into better
enterprise information.
DATA INITIATIVES ARE NOT YET USEFUL
The Information I Need to Do My Job is Available to Me
Percentage of Employees
N = 8,335 employees
Source: CEB 2012 Insight IQ Diagnostic
Information from Corporate Sources is in a Usable Format
Percentage of Employees
33%
67%
Yes
No
46%
54%
Yes
No

16
DATA INITIATIVES FACE LOW EXECUTION CONFIDENCE
The majority of insurers
lack confidence in
analytic capabilities, and
do not have the tools
necessary to assess
analytic needs across
their organization.
Insurers Lack Adequate Means of Determining the Data Tools, Capabilities,
and Quality Needs of Staff
Percentage of Respondents That Do Not Engage in Each Activity
84%
73% 73%
76%
80%
77%
79%
By linking
employee
information
needs to
strategic
decisions
By deriving
employee
information
needs from
strategic goals
or targets
By
establishing
employee
information
needs based
on workflow
processes
By gathering
market or
industry trends
By asking
employees via
survey
By engaging
employees via
focus groups
By tracking
information
requests from
employees
n=198
Source: CEB Financial Services Technology Survey, 2013-2014
Insurers Lack Confidence in their Company’s Ability to Execute Data-Related
Activities
Percentage of Respondents Reporting Little, No, or Neutral Confidence
58%
66% 60% 55%
Data integration Data visualization Data dissemination Data analysis
n=257

17
BIGGER DATA, BIGGER NOISE
As “big data” gets
bigger, it becomes
harder, not easier, for
employees to extract
truly valuable insight
from it.
Source: Taleb, Nassim, “Noise and Signal—Nassim Taleb,” Farham Street Blog, 29 May 2012,
http://www.farhamstreetblog.com/2012/05/noise-and-signal-nassim-taleb/.
Yearly
Data
Low
Low
High
Daily
Data
Quarterly
Data
Hourly
Data
Minute-
Wise Data
Data Big Data
Volume of
Data/Frequency of Data
Observations
Signal
to
Noise
Ratio

18
INTEGRATE BIG DATA STREAMS INTO DAY-TO-DAY
OPERATIONS
Insurers must integrate
big data streams into
core functions and
systems.
Big Data Stream
Core Function and System Integration, 2014
Source: CEB analysis
Underwriter Notes
Medical Reports
E-mail
Telematics
Adjuster Notes
Photos
Social Media
Policy Data
Video
Data Types and Sources (Example)
BigData
Stream
Marketing
Contact
Center
Underwriting ECM Claims
BigDataStream
Marketing
Prior Years 2014

19
Source: CEB FSI Technology Survey, 2013–2014
Sixty percent of
insurance firms affirm
that underwriting
systems technology
provides high or very
high value to their
company.
VALUE OF UNDERWRITING SYSTEMS
Technology Value for Company
Value Drivers
1%
4%
13%
21%
60%
Very low or low
value
Somewhat low
value
Moderate value Somewhat high
value
Very high or high
value
8%
16%
37%
24%
6%
8%
Financial return
on investment
Functionality Process
improvement
Competitive
advantage
Ongoing costs
and
maintenance
Enhancement of
client value

20
Thirty-two percent of
that underwriting
systems technology
poses moderate risk to
their company.
RISK OF UNDERWRITING SYSTEMS
Technology Risk for Company
Risk Drivers
11%
22%
32%
24%
10%
Very low risk Low risk Moderate risk High risk Very high risk
31%
19%
35%
15%
Integration complexity Risk of catastrophic
failure
Information security risk Dependence on
specialized resources

21
Forty percent of
that predictive analytics
technology provides
high or very high value
to their company.
VALUE OF PREDICTIVE ANALYTICS
Technology Value for Company
Value Drivers
0%
9%
27%
24%
40%
Very low or low
value
Somewhat low
value
Moderate value Somewhat high
value
Very high or high
value
26%
10%
18%
38%
8%
0%
Financial return
on investment
Functionality Process
improvement
Competitive
advantage
Ongoing costs
and
maintenance
Enhancement of
client value

22
Thirty-nine percent of
that predictive analytics
technology poses
moderate risk to their
company.
RISK OF PREDICTIVE ANALYTICS
Technology Risk for Company
Risk Drivers
2%
26%
39%
26%
7%
Very low risk Low risk Moderate risk High risk Very high risk
37%
20%
18%
24%
Integration complexity Risk of catastrophic
failure
Information security risk Dependence on
specialized resources

23
Agent and broker specialization requires real time decisioning and
aggregated risk information so that insurers can win new business in a
highly competitive commercial lines space.
A significant underwriter skill gap is looming, and data and analytics can
alleviate serious negative outcomes.
Changing commercial lines financial metrics means insurers must find
new insights for pricing and product innovation.
Complex global supply chain risk can only be managed with integrated
internal and external data.
Integration complexity is the greatest technology risk for analytics and
underwriting system adoption which can be mitigated by partnering with
technology providers with proven capabilities in this area.
TAKE-AWAYS
CEB TowerGroup Insurance

24
Q&A
Insurance
Industry Trends
Commercial
Insurance Risk
Analytics
(CIRA)

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
“60%of insurance firms affirm that underwriting
systems technology provides high or very high value
to their company1.”
Commercial Insurance Risk Analytics:
Harnessing Big Data for Underwriting Efficiencies
Source: 1 CEB FSI Technology Survey, 2013–2014
2 Ordnance Survey “ The big data rush: how data analytics can yield underwriting gold”.
Commercial Insurance Risk Analytics:
Harnessing Big Data for Underwriting Efficiencies
“86% Insurers agree that analyzing multiple-
source data together, rather than separately, is
crucial to making accurate predictions2.”

A wealth of data exists inside and outside the
organization that could improve risk
assessment Geographic and Geo-Spatial
Is the facility located in a site prone to natural disasters?
What is the proximity to Points of Interest?
Political
Is the facility located in a region of political
stability/instability?
Economic
Is the facility located in a high, middle, or low
economic area?
Crime
Is the facility located in a high crime area?
Risk Density
What are the nearby risk factors?
Customer
Personal details, claims history, other policies ?
Claims
How many claims have been made in this area ?

At what level is your organization currently leveraging
external real time data along with internal data in your
underwriting processes?
Select the applicable option
Poll Question 2
1. We have an analytics solution implemented for integrating and
analyzing external and internal data
2. We do not have any solution implemented yet but we are in
process of evaluating various option
3. We are manually pulling data from external sources
4. We are not leveraging external data

The challenge is to integrate large volumes
of varied data and make it accessible
How do I separate the
data I need from the vast
data that exists?
How and where can I
access the data I need?
How do I identify new
data sources to mine for
relevant information? How do I analyze data
in multiple formats
from disparate
sources?
Business impact
Delays and inefficiencies in
collation of data required for
informed decision-making
Inability to treat risks
individually and assess
accurately
Inability to use data proactively
and lack of predictive
capabilities
=
“The majority of insurers lack confidence in their company’s ability to execute
data integration , visualization , dissemination, and analytic capabilities1.”
Source: 1 CEB Research

Commercial Insurance Risk Analytics (CIRA)
Unprecedented access to information on individual risk factors
for a more informed, faster risk assessment
Data Integration,
Analytics, ETL
and data store
HP
HAVEn
Structured and unstructured
data sources
Political
Economic
Crime
Risk Density
Customer
Claims
CIRA Dashboard
Geo-Spatial
Integration of Multiple data sources for
real-time decision making
A “one-stop shop” for collecting, synthesizing, and analyzing risk data utilizing powerful
analytics, database management and a unified framework for data integration and
presentation
Granular Risk Data for
increased accuracy

CIRA Dashboard- “Plug and play” capabilities
display risk data exactly how you want it
• Finely-grained data
from multiple external
sources, integrated with
the insurer’s own data
• Dashboard provides
data visualization and
full drill-down
capabilities into the
underlying data
With Rapid Data Visualization capabilities, CIRA brings the right data in the right format,
customized for underwriters and providing for comprehensive decision support.

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.31
HAVEn
HP Solutions
HAVEn, the HP platform for Big
Data.
Sources:
* McKinsey 2009 data – 2011 “Big data: The next frontier for innovation, competition, and productivity”
** Gartner: Actionable Analytics Will Be Driven by Mobile, Social and Big Data Forces in 2013 and Beyond. Published: 25 January 2013 ID:
G0024716.
*** Gartner: The Information of Things: Why Big Data Will Drive the Value in the Internet of Things Published: 17 April 2013 ID: G00249066.
14.6 PB*
average stored
data per
company >1,000
employees of top
5 industries
Volume
100M**
business events
per second
Variety Velocity
1TB***
amount of data
created by an
industrial
machine per
hour
Big Data’s Big Bang
More and more data to be managed
Big Data Cloud Mobility Security
HP – The New Style of IT

© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.32
100% of your data 1000x faster answers 1.2 month ROI *
1,000,000+
machine events per second
30x
More data per server
700+
connectors
* Source: Forrester Consulting, April 2013
Big Data Cloud Mobility Security
H A V E n
Hadoop/
HDFS
Autonom
y IDOL
Vertica Enterprise
Security
nApps
Catalog massive
volumes of
distributed
data
Process and index
all information
Analyze at
extreme scale in
real-time
Collect & unify
machine data with
ArcSight Logger
Powering HP
Software + your
apps
Social
media
Video Audio Email Texts Mobile Transaction
al data
Documents IT/OT Search
engine
Images
HP HAVEn – Making Sense of the Noise

CIRA empowers underwriters to assess risk
exposure accurately and as a result
improves…
Operational
Efficiency
Claims Cost
Reduction
Risk
Management
Cost
Management
Customer
Experience
CIRA
Platform
Assessment at individual
policy basis increases
accuracy
Unified
Information resulting in
increased efficiencies
and productivity
Identification of
regions with higher
risks optimizes
exposure and
provides opportunities
for claims cost
reduction
Increased
profitability with
lower loss ratios
and ability to price
the risk premiums
more accurately
Ability to offer
attractive terms for
lower risk
customers

More Information
Capgemini Insurance web:
www.capgemini.com/HP/CIRA
HP Capgemini Alliance web:
www.hp.com/go/capgemini
HP HAVEn:
www.hp.com/HAVEn
BriefingsDirect podcast
Solution Brief
Demo video
InsuranceRisk.fsgbu@capgemini.com
Contact us for a Demo presentation
to explore how CIRA solution
provides
value to your business:

CEB Towergroup Insurance Webinar: Empower Underwriters with Data, Analytics and Secret Sauce

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CEB Towergroup Insurance Webinar: Empower Underwriters with Data, Analytics and Secret Sauce