Developing and Deploying Analytics To Improve The Performance Of Commercial Portfolios - VII. International Istanbul Insurance Conference - David Ovenden (2015)

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Developing and deploying analytics to improve the performance
of commercial portfolios
David Ovenden
8 October 2015, Istanbul
VII. International Istanbul Insurance Conference

2. Introduction
“I want to put analytics at the heart of
the commercial business”
UK CEO of a multinational insurer
How can I get the best from scarce commercial data resources?
How much of my portfolio can I afford for my underwriters to touch?
How can I support my underwriter's to make better case level decisions?
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3. Best Practice Underwriting & Pricing Framework
The framework below has been used extensively to benchmark
General Insurers against local and international practices. We have
observed that highly successful organisations execute effectively
across all the components shown below. They then take this a step
further by continuing to invest in a small number of capabilities that
directly relate to their business strategy and ambition.
In the future the successful companies operating competitive markets
will be those who deliver to the market standards in all areas, but then
outperform in technical capabilities that directly address key challenges
and opportunities in their selectedmarkets.
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Underwriting
Strategy
Governance &
Regulation
Capability &
Capacity
Internal &
External Data
Tools &
Techniques
Systems &
Technology
Underwriting
Approach
MI & Decision
Support
Demand
Modelling
Risk Modelling
ANALYSIS
Customer &
Intermediary
Insight
Product &
Portfolio
Management
Price
Assessment
DECISION MAKING & BUSINESS MANAGEMENT
CROSS-FUNCTIONAL
ENGAGEMENT
CROSS-FUNCTIONAL
ENGAGEMENT
PROCESS
Price Delivery Sales Execution
IMPLEMENTATION & DELIVERY
FOUNDATION
PROCESS
Areas of discussion
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4. Supporting underwriter judgement where data is scarce
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 Risks of no / or poor data usage and deployment
 Top line culture drives suboptimal decisions on the frontline
 Battery farmed analytics – teams only looking for “the price“
 The drive for pace reduces thinking time
 Anecdotal evidence and bias drives decisions
 Limited engagement with the pricingteam
 Opportunities – decision support
 Market level context
 Internal case level benchmarking
 Segmental hypotheses become testable
 Test and learn environment (inc. External data)
 Bring the portfolio to life for the organisation
Data can be scarce but valuable, are your commercial teams maximising its potential?
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5. Active prospecting: where am I not?
 Data: Turkey plc
 Market penetration
 Target selection
 Risk level data
 Partner selection
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6. Commercial segmentation – the pricing fountain
Observations
 Data availability and quality is variable
 Different segments require differentapproaches
 Models breakdown at different points
 Sometime communication is an issue
 Premium is a poor proxy for complexity
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Key Questions
 How afford for my underwriters to touch
 Where does my data support automation
 What segments do I have strong data
 How can I best support my underwriters
 How well do my underwriters and actuaries
communicate
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7. Commercial segmentation – decision support
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Commercial clients
Product and regional dashboards
Case level dashboards
 Pricing support
 Scores
 Prioritisation
 Elasticity
 Risks like theses
 Claims insight
 Virtual benchmarks
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Portfolio Level Insight
Delivery of the portfolio level MI,
KPIs and product level
performance and decision
support materials.
Case Level Insight
Automatic provision of case
level decision support for
pricing (e.g. information rich
renewal lists)
Numberofclients
Performance

8.  Developed loss ratio
 Benchmarkprice
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 Deviation to benchmark*
 Percentage ofnon-target
 Portfolio Average Premium
 Portfolio Average per vehicle (or
exposure unit)
 Portfolio Average Loss Ratio
 Performance percentile(mix
adjusted performance
comparison)
 Rating percentile (mix adjusted
rate strength)
 Referral Flag
 Fast track flag
Technical and Analytical Info
Augmented Renewal list
Renewal Date Client Product Current
premium
3 yr Loss
Ratio
1st April Acme Motor Fleet £500 pv 74%
Typical Renewal List
Benchmark
price
Portfolio
level actual
price
Portfolio
level loss
ratio
Financia
l score
Decile Red
Flags
£625 pv £575 pv 68% 120 2 Yes
Augmenting the renewal list…..
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9. Advanced automation producing rapid accurate quotes
Process
filter
“Reality
filters”
Geographic
understanding
Macro universe (TP
data)
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In a highly competitive
environment it is not
enough to just have
good pricing, the active
management of the
portfolio in a cost
effective fashion is
vital.
In an good data
environment it is
possible to generate
high quality segments
on a number of
dimensions. Applying
filters as necessary
to drive focus and
delivery executable
insight.

10. Augmented Renewal lists Examples and screen shots
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12. Underwriting analytics
Datamart(s) Analytics
Wrapper
Quote
engine
Claimsdata
Client exposuredata
Quotation / renewaldata
CaseAnalytics
Interactive caselevel
decisionsupport
Unstructured
support
material
Brokerdata
PortfolioAnalytics
Quotationdata
External Data
Less structureddata
Product MI and UWMetrics
Interactive
Product
dashboard
Interactive
Regional
dashboard
Interactive
Broker
dashboard
ScenarioModelling
Automation
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