Slide deck presenting objectives of Big Data Working group of Institute of Actuaries in Belgium. The goal of the group is to discuss: - Impact of Big Data on insurance sector and the actuarial profession; - Present challenges and good practices when working with Big Data; - Educate actuarial profession about Big Data. Contact me at mat@motosmarty.com

1. MateuszMaj
Chairmanof IABE
BigDataWG
mat@motosmarty.com
Big Data
an actuarial perspective
1st IABEBigDataForum

2. What is Big Data?

3. What is Big Data?

4. What is Big Data?

5. What is Big Data?Big Data
Eric Schmidt,
CEO of Google, 2011
“There
was
5
exabytes
of
informa4on
created
between
the
dawn
of
civiliza4on
through
2003,
but
that
much
informa4on
is
now
created
every
2
days,
and
the
pace
is
increasing.”
But
not
only
the
size
ma0ers
!!!

6. Big Data WG
Why?
Discuss:
• Impact of Big Data on insurance sector and the actuarial profession;
• Present challenges and good practices when working with Big Data;
• Educate actuarial profession about Big Data.

7. Big Data WG
How?
• Big Data information paper ;
• Regular meetings with guest lecturers presenting different
aspects of Big Data, at least bi-monthly;
• Seminars;
• CPD courses – Big Data/Data science program – from 2016;
• Further technical notes on the topic.

8. Insurance value chain:
undewriting
Covers different
Underwriting
360degree
customerview

9. Combine different sources and apply analytics to
create comprehensive customer view and:
• Maximize profitability of the current portfolio
• Detect cross-sell and up-sell opportunities;
• Increase customer satisfaction and loyalty;
• Acquire new profitable customers and reduce marketing costs.
Underwriting

10. Underwriting
Tescogroup–UK
Motor – 1M
Pet – 0.45M
Travel - 0.175M
Life – 0.175M
Home – 0.4M

11. • Insurance prevention program with discounts and rewards for good
driving
• ‘Phased’ approach:
• Phase 1: combine data from different sources i.e. traditional channels, online channels,
external service providers, Tesco group warehouses;
• Phase 2: Identify the right customers within Tesco network;
• Phase 3: Provide initial offer and reward drivers with initial rewards from Tesco group;
• Phase 4: Iterate and provide personalized insurance offers.
Underwriting
makinginsurancesexy

12. Pricing

13. Pricing
Ratingtrends
1980s Now
Profession
Engine power
Coverage
Bonus-malus
Coverage
Bonus-malus
Claims history
Traffic violation
history
Age of vehicle
Use of vehicle
Make of vehicle
Purchase price
Parking place
Occupation
No. of drivers
Age of drivers
Maritial status
Real estate
Driving license
Mileage
Registered owner
Credit rating
…
Do we need additional factors? Is telematics necessary?

14. Univariate
basis
Risk
modelling
Technical
premium
modelling
Scenario
testing
Price
optimisation
Extra data
sources
Telematics
data?
Pricing
Ratingtrends

15. • New rating factors;
• Flexible, dynamic risk pricing;
• New modelling techniques like machine learning;
• New, disruptive insurance offerings like Usage-
Based Insurance.
Pricing

16. Pricing
Usage-BasedInsurance(UBI)
UBI is the scheme where insurance premiums are
calculated based on dynamic causal data, including
actual usage and riskier driving behavior.

17. Insurance value chain:
undewriting
Covers different
Claims management &
Fraud detection
Insurers loose 5% of the annual revenue due to fraud
Coalition Against Insurance Fraud (US) in the 2014 report has
stresses that technology & Big Data plays a growing role in
fighting fraud

18. Claims management
Examples-UBI
From high to low loss ratios
UnipolSai
-­‐
IT CoverBox
&
Carrot
-­‐
UK
Telema'cs
champion
(2.2M
ac've
boxes)
Best
prac'ce
claims
management
incl.:
• FNOL
-­‐
quick
accident
response
• Vehicle
loca'on
in
case
of
of
theG
• Accident
reconstruc'on
Further improvement of the operational
efficiency including:
• Crash data combined with video footage to fight
fraud
• Better prediction methods to reduce claims duration
and cost i.e. no need for expert, efficient accident
reconstruction
• Prove innocence

19. Covers different
Legislation
EU-widelaw
underconstruction

20. Innovation
BigDatacan
boostinnovation

21. Why Change?
- Expensive customer acquisition
- Little contact with customer
- Low brand loyalty and retention
- Regulatory pressure …

22. Insurance
Canitbesexy?
Oscar, US - employs technology, design & data to humanize health care
Habit@t, IT - 1st Connected Home Insurance by Cardiff
Insure the Box, UK – successful UBI with pre-paid model
Intesa SanPaulo Assicura, IT – UBI with viable risk-based pricing model
Friendsurance (DE), Guevara (UK) – P2P insurances
Climate Corp – farmers crop insurance based on high precision weather data

23. EU-widelaw
underconstruction
Role of actuaries
Datascientists
forinsurers
andbeyond

24. MateuszMaj
Chairmanof IABE
BigDataWG
mat@motosmarty.com
Q&A