Transcript Title of Presentation

Insurance Analytics
Pathways for 2015 and Beyond
Karen Pauli
Research Director
CEB TowerGroup
Scott Horwitz
Senior Director
FICO
© 2014 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
Agenda
►Welcome
►Business
Drivers
►Analytics
and Data
►Analytic
Spend and Adoption
►Questions
2
© 2014 Fair Isaac Corporation. Confidential.
Meet Karen Pauli, CEB TowerGroup
Karen is a Research Director in the
Insurance practice at TowerGroup.
She covers a wide range of topics in
property and casualty insurance,
specializing in distribution, underwriting,
claims, predictive analytics, core systems,
and business optimization.
3
© 2014 Fair Isaac Corporation. Confidential.
Insurers are looking for every advantage
they can get to remain competitive and
compliant, and analytics are a key part
of their arsenal.
4
© 2014 Fair Isaac Corporation. Confidential.
ROADMAP FOR THE PRESENTATION
Business Drivers
© 2014 CEB. All Rights Reserved.
Analytics and Data
Analytics Spend &
Adoption
5
INSURANCE BUSINESS, STRATEGIC, AND
TECHNOLOGY PRIORITIES
Business Drivers
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
• 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
Top 10 Technology Initiatives for Insurance
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
Top Life & Annuity Technology Initiatives for Insurance
Manufacture risk solutions with integrated
actuarial systems
© 2014 CEB. All Rights Reserved.
Optimize the value of CRM across diverse
distribution channels
Integrate consumer and
distributor portals with
back-end technology
Leverage increasing variety
of core system delivery
options
Create a device-agnostic
mobile infrastructure
Top Property & Casualty Technology Initiatives for
Insurance
Integrate big data streams into day-today operations
Expand telematics applications beyond
personal auto
Source: CEB Analysis
6
INSURANCE BUSINESS, STRATEGIC, AND
TECHNOLOGY PRIORITIES
Business Drivers
Strategic Responses
• 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
Top 10 Technology Initiatives for Insurance
Life & Annuity and Property & Casualty
Top Life & Annuity Technology Initiatives for Insurance
© 2014 CEB. All Rights Reserved.
Top Property & Casualty Technology Initiatives for
Insurance
Source: CEB Analysis
7
EVOLVING INDIVIDUAL SALES AND
SERVICE EXPECTATIONS
Consumers’ expectations
for sales and service are
changing, exemplified by
a rapid change in
channel preferences.
Channel Preferences for Using or Accessing Financial Products and Services
North American Consumers, 2010 and 2013
53%
+8%
45%
34%
30%
-4%
Agent
Online
2010
2013
2010 n = 1,850; 2013 n = 2,713
Source: CEB 2011 and 2013 Customer Experience Surveys
© 2014 CEB. All Rights Reserved.
8
CHANGING DISTRIBUTOR BUSINESS MODELS
Distributor business
models are shifting and
show an increase in the
specialization of
independent agents and
brokers.
Agency Specialization
Percentage of Survey Respondents Reporting an Increase in Specialization, by
Revenue Group, 2010 and 2013
80%
73%
72%
64%
56%
68%
64%
56%
51%
44%
39%
25%
<$1.25 million $1.25–2.5 million $2.5–5 million
$5–10 million
2010
2013
$10–25 million
>$25 million
Source: IIABA’s 2013 Best Practices Study
© 2014 CEB. All Rights Reserved.
9
CONTENTIOUS SCOPE AND AUTHORITY OF
INSURANCE REGULATORS
Insurers feel the strain
of conflicting
regulatory bodies in
the U.S. and
internationally.
Global Insurance Regulation
Illustrative Example, Solvency Regulation Requirements, U.S. Impact
State
Federal
International
RMORSA
Risk Management and
Own Risk Solvency
Assessment Model Act
FIO Modernization Report
Released December 2013
Solvency II
Implementation date: 2016
State law requiring insurers
to implement an enterprise
risk management
framework by January
2015
Includes recommendations
for:
• Material solvency
oversight decisions of a
discretionary nature
• Improved consistency
of solvency oversight at
the state level
Risk-based approach to
capital requirements for
insurers, three pillars:
1) Quantitative risk-based
capital requirements;
2) System of governance;
3) Supervisory reporting
and disclosure of
information
Source: CEB Analysis
© 2014 CEB. All Rights Reserved.
10
COMPETITION FOR CRITICAL SKILL SETS INTENSIFIES
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.
Q: How concerned are you about the availability of key skills as a business threat?
Percentage of Respondents, 2011 and 2012
2011
2012
13%
10%
33%
31%
39%
41%
15%
17%
Not at all concerned
Not very concerned
Somewhat concerned
Extremely concerned
n = 1,330
Source: PwC 15th and 16th Annual Global CEO Surveys
© 2014 CEB. All Rights Reserved.
11
GLOBAL DEPENDENCE ON VOLATILE REGIONAL
ECONOMIES
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.
Fluctuations Across Economies
Percent Change in GDP over Corresponding Period of Previous Year, Advanced
and Emerging & Developing Economies, 1998–2012
10%
5%
0%
-5%
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Advanced Economies
Emerging and Developing Countries
Source: IMF
Economic Interdependence
US Impact of 2011 Thailand Floods, Illustrative
Thailand Floods, 2011
Total Insured Loss
(USD millions): $15,315
Decrease in Thailand
manufacturing output due
to factory closures
Impact on US Business:
Technology and Auto
Manufacturers & Suppliers
Approximately 1,007 (billions THB) in
economic losses in manufacturing
Hewlett Packard: 3.5%+ decline in 2011
revenue
Ford: $80 million loss
Source: Insurance Information Institute, Bloomberg, Aon Benfield
© 2014 CEB. All Rights Reserved.
12
ROADMAP FOR THE PRESENTATION
Business Drivers
© 2014 CEB. All Rights Reserved.
Analytics and
Data
Analytics Spend &
Adoption
13
MORE INFORMATION, MORE INFORMATION WORK
“Big data” is quickly
becoming a reality as
information volumes
grow by 60% annually,
and 36% of all work time
is devoted to information
collection and analysis.
Estimated Rise in Global Data Volumes, 2010–2015
Indexed to 100
1,200
1,050
660
600
0
410
100
160
2010
2011
260
2012
2013
2014
2015
Source: CEB analysis.
Time Spent Collecting and Analyzing
Information
Percentage of Total Knowledge Worker Work
Time
64%
All Other
Work
36%
Collecting
and
Analyzing
Information
n = 4,941 knowledge workers.
Source: “All Too Much” The Economist, 27 February 2010; CEB analysis.
© 2014 CEB. All Rights Reserved.
Drivers of Democratized Decision
Making
Decisions are made closer to the
market
(e.g., product design, channel mix).
2 Decisions are more dynamic and
varied
(e.g., demand forecasts, discounts).
3 Knowledge workers have access to more
information and better tools (e.g.,
customer segmentation and value analysis).
1
Source: CEB analysis.
14
BIGGER DATA, BIGGER NOISE
As “big data” gets
bigger, it becomes
harder, not easier, for
employees to extract
truly valuable insight
from it.
High
Data
Big Data
Signal to
Noise Ratio
Low
Yearly
Data
Low
Quarterly
Data
Daily
Data
Hourly
Data
MinuteWise Data
Volume of
Data/Frequency of Data
Observations
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/.
© 2014 CEB. All Rights Reserved.
15
REPORTING AND ANALYTICS MATURITY
Only 7% of insurance
executives report having
a mature and optimized
process in place for
reporting and analytics.
How would you assess your company’s maturity level in reporting and analytics?
Percentage of Respondents, 2013
Reporting and Analytics: Reporting refers to the process of converting data into a normalized, structured,
and actionable representation. Analytics refers to the systematic processing of data or statistics to produce
insights supporting a business decision.
37%
31%
16%
10%
7%
Initial (chaotic, ad
hoc)
Repeatable (a
documented
process)
Defined (defined
and confirmed
standard process)
Managed (process Mature/Optimizing
is quantitatively
(deliberate process
managed with
with optimization
agreed upon
and improvements)
metrics)
n=257
Source: CEB 2013-2014 FSI Technology Survey
© 2014 CEB. All Rights Reserved.
16
Over 50% of executives
attribute high
importance to all
analytics functions, but
their confidence in
execution is low.
IMPORTANCE OF ANALYTICS FUNCTIONS DOES NOT
MATCH EXECUTION CONFIDENCE
Importance and Confidence in Execution Attributed to Analytics Functions
Percentage of Respondents, 2013
Importance
70%
64%
62%
61%
Confidence
61%
61%
60%
57%
54%
50%
40%
30%
20%
23%
23%
26%
19%
20%
20%
17%
10%
0%
Improving
Risk
product or management
service
profitability
© 2014 CEB. All Rights Reserved.
Forecasting
demand for
products,
services,
resources
(e.g. pricing
analytics)
New market
Product or Developing a Evaluating
identification
service
corporate or
and
and market development business unit prioritizing
strategy
strategy
investment
development
proposals
Importance question: How important are each of the following analytics functions to your company’s operations?
Confidence question: How much confidence do you have in your company’s ability to perform the following analytics functions?
n = 257
Source: CEB 2013-2014 FSI Technology Survey
17
CENTRALIZE MANAGEMENT, NOT INFORMATION
Foundational analytic
and information
management activities
benefit from
centralization and create
sufficiently strong
oversight to sustain
decentralized information
sources.
Maximum Impact on Insight IQa of Centralized Models for Information Management
14.0%
13.0%
10.5%
10.4%
6.9%
7.0%
6.7%
0.0%
0.0%
Centralized Centralized
Analytics Knowledge
Team
Worker
Training
Centralized Centralized Centralized
Information User Support Information
for Analytic Architecture
Quality
Tools
Organizations with a high insight IQ centralize
information management and support
activities…
© 2014 CEB. All Rights Reserved.
0.0%
Single
Single Data
Unstructured Warehouse
Information
Repository
…which enables them to keep
the information itself
decentralized.
n = 4,941 knowledge workers.
a: The maximum impact on Insight IQ is calculated by comparing two statistical estimates: the predicted impact when a knowledge
worker scores relatively “high” on a driver and the predicted impact when a knowledge worker scores “low” on a driver. The effect of
each driver is modeled using a variety of multivariate regressions with controls.
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Source: CEB 2011 Insight IQ Diagnostic.
CREATE A HORIZONTAL ENTERPRISE ANALYTICS AND
MODELS LAYER
As critical mass in
analytics is reached,
insurers need to
abandon their siloed
approach to analytics
adoption and integration
and aggregate analytics
tools into a horizontal
layer of analytics and
models that are utilized
enterprise-wide.
Analytics Adoption and Replacement
Siloed Approach, Prior Years
Historically, insurers had a siloed approach to analytics adoption and integration
Marketing and Product
Development
Distribution and Sales
Policy Administration
Analytics Tools
Analytics Tools
Analytics Tools
Claims Processing
Infrastructure/Support
Analytics Tools
Analytics Tools
Integrated Approach, 2014
In 2014, insurers need to create a horizontal layer of analytics and models tools that are utilized across the enterprise
Marketing
and
Product
Development
Source: CEB Analysis
© 2014 CEB. All Rights Reserved.
Distribution
and
Sales
Policy
Administration
Claims
Processing
•
•
•
•
Infrastructure/
Support
Horizontal Enterprise
Analytics and Models Layer
Predictive Analytics
Operational Analytics
Consumer and Marketing Analytics
Pricing Optimization
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ROADMAP FOR THE PRESENTATION
Business Drivers
© 2014 CEB. All Rights Reserved.
Analytics and Data
Analytics Spend
& Adoption
20
SPENDING ON PREDICTIVE ANALYTICS
Forty-three percent of
insurance firms expect
spending on predictive
analytics technology to
increase in the next 2
years.
Expected IT Spend Change by 2015
Percentage of Respondents, 2013
43%
21%
21%
14%
Decrease
Little or no change
Increase
Unsure
n=67
Source: CEB 2013-2014 FSI Technology Survey
© 2014 CEB. All Rights Reserved.
21
STATE OF TECHNOLOGY: PREDICTIVE ANALYTICS
Forty-one percent of
insurance firms intend
to adopt or replace the
technology before
2018.
State of
Technology
Current State of Technology Implementation by 2018
Percentage of Respondents, 2013
39%
Definitions
Does not have
it
My company does NOT use
technology in this area and
DOES NOT intend to install the
technology by 2018.
Adopting
Until recently, my company had
no technology for this area, but
HAS adopted the technology in
the past 12 months or WILL by
2018.
Have it, no
change
My company has technology in
this area, DID NOT make a
major system replacement in the
past 12 months, and WILL NOT
before 2018.
Replacing
My company has had technology
in this area for over a year, and
DID make a major replacement
in the past 12 months or WILL
make one by 2018.
25%
16%
Replacing
© 2014 CEB. All Rights Reserved.
Adopting
Have it, no change
9%
10%
Does not have it
Unsure
n=67
Source: CEB 2013-2014 FSI Technology Survey
22
PREDICTIVE ANALYTICS VERY HIGH OR HIGH VALUE;
COMPETITIVE ADVANTAGE
Forty percent of
insurance firms affirm
that predictive
analytics technology
provides high or very
high value to their
company, which is
primarily due to the
innovative new
insights these tools
provide.
Technology Value for Company
40%
27%
24%
9%
0%
Very low or low
value
Somewhat low
value
Moderate value
Somewhat high
value
Very high or high
value
Value Drivers
38%
26%
18%
10%
8%
0%
Financial return
on investment
Functionality
Process
improvement
Competitive
advantage
Ongoing costs Enhancement of
and
client value
maintenance
n=67
Source: CEB 2013-2014 FSI Technology Survey
© 2014 CEB. All Rights Reserved.
23
PREDICTIVE ANALYTICS MODERATE RISK;
INTEGRATION COMPLEXITY
Thirty-nine percent of
insurance firms affirm
that predictive analytics
technology poses only
moderate risk to their
company.
Technology Risk for Company
39%
26%
26%
7%
2%
Very low risk
Low risk
Moderate risk
High risk
Very high risk
Risk Drivers
37%
Integration complexity
24%
20%
18%
Risk of catastrophic
failure
Information security risk
Dependence on
specialized resources
n=67
Source: CEB 2013-2014 FSI Technology Survey
© 2014 CEB. All Rights Reserved.
24
Questions
25
© 2014 Fair Isaac Corporation. Confidential.
26
Thank You!
Scott Horwitz
[email protected]
Phone
© 2014 Fair Isaac Corporation. Confidential.
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© 2014 Fair Isaac Corporation. Confidential.