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How AI is making insurers sharper Nicholas Warren Niche Ideas 2018
How AI is making insurers
sharper
Nicholas Warren
Niche Ideas 2018
Road Map
What is AI?
Reasons you can’t ignore it
Some real world examples
A few words of warning
What the future holds
What is AI? … or what isn’t AI?
What is AI?
What is AI?
Machine learning is a sophisticated algorithm which uses the
data provided to identify patterns.
Artificial Intelligence Philosophical idea
Machine Learning
Algorithm
Reasons not to ignore AI
Four dimensions of change
Data Volume
Becoming essential. Traditional analysis is time
and resource intensive.
Uncovering Complexity
Identify complex drivers behind relationships.
Cost
Cheaper due to reduced manual intervention and open source software.
Accuracy
Predict things we never thought possible until
recently.
Real world examples Marketing effectiveness
Marketing effectiveness Life and Health Insurer commenced a Membership Rewards Program to:
“Reward and engage their health insurance customers”
Very strong push from management to build a “critical mass” – a deadline to achieve an uptake of 20,000 customers.
Had undertaken an aggressive marketing campaign across multiple media channels.
The problem Conversion rates were too low.
Weren't going to meet the target in the 4 weeks before the deadline.
Marketing effectiveness
• Customers assessed at an individual level based on unique combination of characteristics
• Prioritise contacts and develop targeted eDM and phone campaigns
SCV + External Data Current Campaign
Combine SCV, External Data and current Campaign results
Machine Learning Model
Most Likely Least Likely
Marketing effectiveness
“Our conversion effectiveness increased by 55% and cost efficiency
improved by 33%. Plus we hit our membership target a week before
deadline and we were 10% above target by the deadline date”
Awards: • 3 x Gold NZ Direct Marketing Awards:
(Customer & market insight, direct response, excellence in data strategy)
• TVNZ Awards – best used of customer insight and data.
• International Direct Marketing Awards - finalist
Real world examples Sales analysis
Sales analysis Large insurer wanted to better understand where it was winning and losing it’s
commercial business.
What region and products had better or worse results?
What was the impact of different brokers?
What was the impact of different discounting?
Sales analysis
Build Machine Learning
model
Extract, clean & augment
quotes database
Machine Learning
- Analysed entire quote/proposals database
- identified what made a quote more/less likely to convert into new business.
40
1
1.5
2
2.5
3
3.5
0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%
Re
lati
ve
Ch
an
ge
in
Co
nve
rsio
n R
ate
Premium Discount (%)
Converstion Rate Price Elasticity Curves by Segment
Segment 1 Segment 2 Segment 3 Segment 4
Segment 5 Segment 6 Segment 7 Segment 8
Who was sensitive to price?
Sales analysis
DR: Segment Discount Rate; Exp: Proportion of quote exposure
#Risks >=4
#Risks >=6Comm. Prop. Total Premium >=$4000
#Risks >=2Total Comm Prop. SI
>=$80000
%Premium From Comm. Prop. >=82.54%
Sub-Region not in (Christchurch, Auckland, Takapuna, East Auckland)
YES NO
YES NO YES NO
Segment 5DR:-2.8%
Exp: 10.4%
Segment 3DR:-4.0%
Exp: 15.9%
Segment 7DR:-1.8%Exp: 7.2%
Segment 8DR:-1.1%Exp: 4.0%
Segment 4DR:-3.8%
Exp: 17.0%
Segment 1DR:-6.9%
Exp: 22.8%
Segment 2DR:-4.3%
Exp: 15.1%
Segment 6DR:-2.3%Exp: 7.6%
YES NOYES NOYES NOYES NO
Who is being given a discount?
Align where discounts given with price elasticity and strategic direction.
Real world examples Campervan Rental
Campervan Rental Pricing
• Large company headquartered within New Zealand which
manufactures and hires campervans
• Operations across Australia, New Zealand, UK and US
• Listed company within the NZX 50
Rental Pricing – a big guess?
Copyright© Finity Consulting Pty Ltd 43
Campervan Rental Pricing
Travel duration Time until travel
Travel Day
Fleet Capacity
Substitution (upgrade?)
International vs Domestic
Competitor Prices
Expected Demand
Product & Location
Systematically update prices to reflect the latest information on demand, yield, utilisation and competitor price.
Machine Learning Framework
Demand (expected vs forecast)
Yield (current vs forecast)
Utilisation (booked vs capacity)
Competitor (vs Comp. price)
Pricing
Response (Unique by travel
day, product,
branch,
international)
Current
Prices
Updated
Price
Rental Pricing
0
100
200
300
400
500
600
Jan-18 Mar-18 May-18 Jun-18 Aug-18 Oct-18 Nov-18 Jan-19 Mar-19
Pri
ce
($
)
Date of Travel
Evolution of Future Travel Date PricesProduct A: Location X
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9
A word of warning
What could possibly go wrong?
Intelligent learning doesn’t mean thoughtful
and critically discerning
What does the future hold?
• The Mores laws – Moore’s law
– Mores law
– More Law?
– Just because you can doesn’t mean you should
– Better pricing - when does insurance stop being insurance?
Moore’s Law
Computers will continue to get faster.
People will expect their insurance products will
keep up.
More’s Law
More data, more sophistication, more
appetite.
More Law
Increased regulation and scrutiny over data privacy
and algorithm bias.
Conclusions
Data volumes
Complex relationships
Cost
Accuracy
Traditional/Historically Machine Learning
Time and resource intensive.
Impossible for large data.
Challenging to detect.
Manual intervention.
Expensive software.
Good.
Efficient across large datasets.
Identify complex relationships.
Open source software and reduced
development time cost.
Better and more reliable.
Distribution & Use
The information in this presentation is being provided to
participants at Finity’s Niche Ideas Conference on 21 March
2018.
The content of all presentations are for the purpose of
discussion and debate at the conference. The presentation
is not intended, nor necessarily suitable, for any other
purpose.
The contents of this presentation are not to be used for any
other purpose and are not to be distributed to or discussed
except within the participant company or organisation.
Third parties, whether authorised or not to receive this
presentation, should recognise that the furnishing of this
presentation is not a substitute for their own due diligence
and should place no reliance on this presentation or the data
contained herein which would result in the creation of any
duty or liability by Finity to the third party.
Reliance and Limitations
The information in this presentation is being provided on a
confidential basis to assist the company to understand the
nature of the analysis and output proposed.
We have relied on the accuracy and completeness of all data
and other information (qualitative, quantitative, written and
verbal) provided to us for the purpose of this presentation. We
have not independently verified or audited the data but we
have reviewed it for general reasonableness and consistency.
It should be noted that if any data or other information is
inaccurate or incomplete, we should be advised, so that our
advice can be revised, if warranted.
