1. 1
Best Practices in Data and
Analytics
Industrial Strength
Analytics in the Age of
Big Data
May-2015
Paul Codd

2. One community protecting what matters
Personal Insurance
How can we move to this new world?
Imagine a world where …
2
 Engaged
 Enabled
 No/Low turn over
 Low absenteeism
 Award winning
team members
Employees
 More efficient
 More productive
Team
 Market share
growth
 Profit growth
 New Business
growth
 P&L improvement
Financial Performance

3. One community protecting what matters
Personal Insurance
What is the role of the Analyst when a computer does all of the
analysis?
Imagine a world where …
3
 Is automatic and
seamless
 Each month’s new
experiences
automatically joins
to existing data set
 Big, new data is
easily incorporated
into prep
Model Data Prep
 Model
automatically build
 Models
automatically
update monthly
Model Build
 Simple dashboards
highlighting top
down performance
 Simple dashboards
highlighting bottom
up model
performance
 Segments
highlighted and
ranked for
investigation
Model Monitoring

4. Markets are becoming more competitive & transparent
4
 Products are commoditising
 New Channels
 Aggregators Sites
 Internet search is increasing transparency and reducing search costs
 Major players using customer reach to enter non-traditional markets
 12 New Entrants in Personal Insurance market over past 36 months
 Underlying risk characteristics are also evolving
 Non-traditional pricing factors being used/tested
 Behavioural traits emerging as key pricing factors
 Technology enhancements improving risk outcomes
Our Market is Changing
The Australian, 17 October 2013

5. Pricing is becoming more sophisticated, enabled in part
by technology
5
Our Market is Changing
 Incorporation of big data into
modelling
 Woolworths (Quantium) Coles (Fly Buys)
 Customer factors
 Risk factors
 Introduction of Customer
Demand models, competitor
views and Optimisations
 Switching Behaviour
 better knowledge of propensity to
shop (elasicity). Knowledge of where
they are shopping and competitor
price
 Advancement of technical rating
 GLM, Cart, Random Forests, Machine
Learning
Technology Capability is Expanding
 Cloud computing
 Netezza boxes
 SQL Servers
 Networking
 Open Source (R)
 Python
 Laptop
 32G, 8 core
Processes are getting more complicated, but
speed to market is critical for future success

6. Complexity increased with our Portfolio of Brands
6
Portfolio of Brands

7. Pricing eco-system straining under this complexity
7
Risk Pricing
Market Based
Pricing
Portfolio
Analytics and
Monitoring

8. Technical Pricing
8
Data Prep
Model Build
Implementation
Monitoring
Technical Pricing
Cycle
Motor Home M.Cycle Boat Caravan Travel
AAMI
APIA
GIO
SUN
Vero
Resilium
Bingle
JCI
Shannons
CIL
IMR
TSI
Brands
Products
Product Brand Martix
Peril Based
Pricing
Perils Freq (%) Size ($)
AFC
SCC
DWP
ST
NAF
Peril Pricing

9. 9
Motor Home M.Cycle Boat Caravan Travel
AAMI
APIA
GIO
SUN
Vero
Resilium
Bingle
JCI
Shannons
CIL
IMR
TSI
Brands
Products
Product Brand Martix
Portfolio Analytics and Monitoring
Monthly Monitoring
 Top down portfolio view
 New Business
 Strike rate, Av offered premium,
Av Paid premium
 Renewals
 Lapse, cancellation, av
premiums
 Claims
 CF, av claim size, CPP, LR

10. 10
Motor Home M.Cycle Boat Caravan Travel
AAMI
APIA
GIO
SUN
Vero
Resilium
Bingle
JCI
Shannons
CIL
IMR
TSI
Brands
Products
Product Brand Martix
Portfolio Analytics and Monitoring
 New Business
 Strike rate, Av offered premium,
Av Paid premium
 Renewals
 Lapse, cancellation, av
premiums
 Claims
 CF, av claim size, CPP, LR

11. Market Based Pricing
11
Strike Rate
LossRatio
Segment Pricing
P
P
Defend
Attack
Adjust
Withdraw

12. Market Based Pricing
12
Segment Pricing
P
P

13. Pathway to Industrialisation
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Monthly processing into single Data WarehouseData Prep Phase
Develop a single portfolio analytics platform
Build technical models for all products (brand interactions)
Build Models
Automation Phase
The roles of staff are changingTeam Dynamics
Data Prep
Self-
Adapting
Models
Predictive
Insights

14. Self-Adapting Models (SAMs)
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1. Apply previous
model to new data
2. Test simple and
custom factors
and interactions
3. Test new factors
4. Test curves and
interactions
5. Calculate
credabilities
6. Incorporate
changes into
previous model
7. Set an
appropriate mean.
Model complete
MonitoringUpdating

15. Predictive Insights
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Factor Segment Measure 1 Measure 2 Rank
Purchase Price $20,000 18.81 182,209 1

16. Previous Best Practice New Best Practice
Technical Models Updated Annually Weekly, Monthly
Modelling Techniques GLM (simple)
Hybrid models,
ensemble models,
machine learning, GLM
(complex)
Implementation Weeks, Months Days
Time to Include New Factors Weeks, Months Hours
Process Manual Automated
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Best practice is changing in all aspects of Pricing