The Future Of Underwriting Transformation by Talent & Technology - Sanda Cagalj EY Advisory (2015)

1. The Future of Underwriting
Transformation by Talent & Technology
Sanda Cagalj
EYAdvisory
Sanda.Cagalj@tr.ey.com

2. Contents
Key drivers of the future
Transformative role of Underwriter
Development of Technology
Influencing Role of Underwriter
• Data Collection Tools
• Business Solution Tools
1
2
3

3. Key drivers of the future
Overview
Integrated systemsand
processes
Key Trends
Automationin
Underwriting
Innovative products and
services
3 The Future of Underwriting
Pricing optimization
Digital Future –focus
on analytics and BI
Personalized customer
experience

4. Financial Services lags other industries in
analytics
Lack of historical investment, poor data quality and limited commercial imperative have meant Financial services lags other industries
Analytics maturity modelValue
Most companies
are here
Analytics maturity
Leading
► Data: Relentless pursuit for new
data and metrics
► Data viewed as strategic
asset
► People/Talent: Strong leaders
behaving analytically and
showing passion for analytical
competition
► Initiatives: Analytics integral to
company strategy
► Enterprise approach: Analytics
tools and infrastructure
extended broadly and deeply
across enterprise
Nascent Developing
► Data: BU-level data, data
management not a key
priority
► People/Talent: Pockets of
analytics excellence but not
pervasive
► Initiatives: Multiple targets,not
all of strategic importance
► Lack of global processes
► Limited standardized
reporting
► Enterprise approach: Localized
analytics, local value
Established
► Data: Data virtualization –
Identifying key data
domains/central data
repositories, 360 customer
views
► People/Talent: Senior leaders
recognize importance of
analytics and developing
analytic capabilities
► Initiatives: Global processes
against small set of strategic
targets
► Enterprise approach: Analytics
embedded in all levels of
decision making
► COE/operating model to
enable
Advanced
4 The Future of Underwriting

5. Supply and demand of deep analytical talent by
2018
150
180 30
300
140-190 440-490
2008
employement
5 The Future of Underwriting
Graduares
with deep
analytical
talent
Others* 2008 supply Talent gap 2018 projected
demand
* Other supply drivers include attrition(-), immigration (+) and reemploying previously unemployed deep analytical talent (+).
Source: US Bureau of Labor Statistics; US Census; Dun & Bradstreet, company interviews; McKinsey Global Institiude analysis
Thousand
people
Demand for deep analytical talent in the United States could be 50 to 60 percent greater than its projected supply by 2018

6. What are your biggest barriers to using analytics
more frequently to inform decision making?
0%
6 The Future of Underwriting
5% 30% 35% 40% 45%
Other
Math skills to do analysis
None. We're a data-driven organization
Expense
What time. My Organization doesn't have analytics tools I can use
without going through IT/business analytics
Access to an analytics tool that is simple for me to use
Access to the data to do analysis
10% 15% 20% 25%
% Respondents

7. What do you want to accomplish in your
organization with analytics?
00%
7 The Future of Underwriting
05% 10% 15% 20% 40% 45% 50%
Improve quality control
Improve IT processes and procurement
Improve sales processes
Improve customer targeting and personlization
Assess financial risk
Evaluate employee performance
Anticipate product demand
Improve logistics
Gain insight into marketing campaigns
Reduce customer churn
Improve employee recruitment and retention
Anticipate equipment maintenance
Other
25% 30% 35%
% Respondents

8. WHO LOSES
What do Best Practices suggest
What do the best ones do
WHO WINS
8 The Future of Underwriting
► Investing in new underwriting analytical
capabilities to use new data sources and
interfaces to identify important trends,
opportunities and risks
► Investing in powerful emerging technologies
including sensors, telematics, web and mobile,
new pricing techniques
► Integrating sales and underwriting portals
► Underwriter as:
► Sales executive
► Decision maker using data aggregator
services and predictive analytics
► Customer advocate by increasing customer
loyalty through better solutions which
ultimately means higher account retention
► Innovator seeking for innovation in product
development, customer engaged
► Convectional role of underwriter focused on
calculating hit and retention ratios, annualized
growth and calendar year and accident year loss
ratio
► No investment in right technology tool sets

9. 9
Are you leveraging data and analytics fully
to transform every aspect of your business?
Are you READY?

10. 10
Key Questions Insurers Are Asking?
What more can our
own data tell us?
02
What else could we
learn if we added
external data to our
models?
01 03
How can we build the
power of analytics into
day to day decision
making?
From data comes information, from information comes knowledge, and from
knowledge, power”
– Gail Jones, RGA

11. Innovate business models for insurers
Price Optimization
Real Time Pricing
Underwriting Automation and Optimization
Sensor Technology
Telematics
Straight – through
Processing
U.S. Commercial Insurer
How can I apply additional
data sources to improve the underwriting process?
Mastered risk location and
merged additional data
sources to assess probable
maximum losses (PMLs)?
Improved combined ratio and reduced cost of
reinsurance
11 The Future of Underwriting

12. Core capabilities of the Telematics Insurer
Capabilities of the
telematics
insurance model
12 The Future of Underwriting
Predictive
modeling
Data
Managament
Telematics
technology
expertise
Customer
İnsight and
Relationship
management
Customer
Oriented product
Development and
marketing
Partnerships
with telematics
ecosystem
constituencies Staff selection
and training
Business
process
management
Web 2.0
capabilities
Data and
analytics
Process and
technology
People and
partnerships
Customer centricity
Underwriting
Optimization
Underwiting
Automation

13. Telematics - expense cost for an average motor
insurance policy
Underwriting
Optimization
Underwiting
Automation
Telematics increases costs by 57% onaverage
40 40
13,5 13,5
36
50
0
20
60
40
100
80
36
160
140
120
Traditional Telematics
Acquisition Levies Admin Box Data
Expensesperpolocy
GBP
Source: S&P SynThesys, Deutsche Bank estimates
Expense difference is a lesser issue for high
value policies
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
AdditionalExpense(%ptsofpolicy
premium)
Policy
Premium
Source: Deutsche Bank
57%
13 The Future of Underwriting

14. Reason why insurers shouldn’t underestimate
insurance telematics
Smartphones
accounted for 57.6
percent of total sales in
Q4 / 2013
Apple partnered wiht 15+
OEMs to bring the iphone
experience and
functionality to the car
In 2014 Google introduced the
Open Automotive Alliance.
On board: Audi GM, Honda,
Hyundai
69% of Americans use
quantified – self applications.
2 of 3 Europeans are
interested in such
applications
0
20
60
40
80
100
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Europe US China
Based on known OEM plans
14 The Future of Underwriting
Based on assumptions
Annual fitment rate of embedded telematics devices (%) across
Europe, US and China by 2020
Underwriting
Optimization
Underwiting
Automation

15. Straight – through Processing
Functionalities
provided
under
automation
underwriting
End - to - end
transparency
Automated
risk
analysis Integration
with existing
platforms
Modernization
Straight –
through
processing
Source: Analysis, 2012; Celent Model Insurer Asia 2011
15 The Future of Underwriting
Functionalities embedded in automated underwriting systems
Underwriting
Optimization
Underwiting
Automation

16. Pricing sophistication project is expected to move your
analytical maturity state from ‘nascent’ to ‘established’
Nascent
Descriptive analytics
► Leverage historic data analysis to report and visualize WHAT has happened
► Typically have a data cube to enable data mining and production ofreports
Predictive analytics
► Leverage past data to understand the underlying relationship between data inputs and outputs to understand WHY
something happened and predict WHAT will happen in the future given various scenarios.
► Use of multi-variate statistical models to identify optimal price structure to maximiseprofits
► Embedded price optimisation process to determine a set of pricing actions to produce the most effectiveresults
Established
Prescriptive analytics - proving enterprise insight
► To determine WHICH decision or action across the enterprise that will produce the most effective results against a
specific set of strategic objectives
► Commercial planning targets by product to drive a pricing and operationalstrategy
► Regular monitoring of conversion, elasticity and market movements at a customer segment level
► Dashboard to identify portfolio performance
Leading
16 The Future of Underwriting

17. Pricing – Demand-Based vs. Risk-Based
Aligning Insand Customer Views
Competitio
n
Brand
Reliability
Convenienc
e
Loyalty
Enable insurers to offer the optimal price for each customer
Operational
Cost
Margin
Lost Cost
2
Optimize the pricesto
achieve corporate goals
3
Input the costs
1
Model customer
behavior
I n s u r e r C u s t o m e r
Underwriting
Optimization
17 The Future of Underwriting
Underwiting
Automation

18. Increase Profit and Grow
R e t e n t i o n
22 22.8 23.6 24,4 25.2 26 26,8 27,6 28,4 29,2 30 30,8 31,6 32,4
88.40
88.90
88.65
89.15
89.90
89.65
89.40
90.15
90.40
90.65
88.15
87.90
We are here
0.85% AddedRetention,
$3.1M Added Earnings
$6.1M AddedEarnings,
Same Retention
1.5% AddedRetention,
Same Earnings
18 The Future of Underwriting
Underwriting
Optimization
Underwiting
Automation
E a r n i n g s

19. The Pricing Optimization Cycle
G O AL : optimize prices in order to maximize profit while achieving defined corporate
performance objectives - business growth, volume targets,retention
> Demand models
> Cost/risk models
> Profitability model
(function to optimize)
> Additional variables
(for KPIs,
constraints, other)
> Run what-if scenarios
> Analyze compare
and choose
Data
Management
Demand
Estimation
Price &
Strategy
Optimization
Monitoring
&
Recalibration
Data Feed
Chosen Pricing
Strategy
Price Execution
19 The Future of Underwriting

20. Pricing Management: End-to-end Pricing Process,
Multiple Products
G e o An a l y t i c s
E x e c u t i v e D a s h b o a r d
Fully integrated workflow
All Insurance LoBs
in one instance:
• Auto
• Home
• SME
Commercial
• Other
˃ Risk
Premium
Risk
(Loss Cost)
˃ What if
˃ Individual PO
˃ Factor PO
Price
Optimisation
˃ Execution
Manager
˃ Real Time
PO
Price
Execution
20 The Future of Underwriting

21. Key Benefits
P r o f i t s
> Improves profits by 1-3% of GWP For $100M GWP,profit
improves in $1-3M, or Combined Raito decreases by 1-3%.
> Typical ROI greater than X10 achieved within first year
> Balance profitability and retention to deliver corporategoals
> Better management decisions through improved understandingof
pricing strategies and their implication on businessKPIs
> Improved understanding of customer behavior
D e c i s i o n s
> Faster time to market of prices & products through real time
online integration
> Improved pricing processes and more efficient work of
pricing analysts
> Increased collaboration across departments
P r o c e s s e s
21 The Future of Underwriting

22. Price Optimization
Source: Earnix 2012 North America
Pricing Survey
22 The Future of Underwriting
Earnix 2012 North America Pricing Survey
50 respondents, Home & Auto
Earnix 2012 EMEA
Pricing Survey
110 respondents, Home, Auto and SMB
Source: Earnix 2012 EMEA Pricing Survey
Underwriting
Optimization
Underwiting
Automation

23. THANK YOU!
Sanda Cagalj
EYAdvisory
Sanda.Cagalj@tr.ey.com

24. EY | Assurance | Tax | Transactions |Advisory
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