This document discusses how big data is impacting the insurance industry. It covers how insurers are using big data across the insurance value chain, from underwriting to pricing to claims management and fraud detection. Insurers are able to create more comprehensive customer profiles by combining internal and external data sources. This allows for more personalized insurance offerings and pricing models like usage-based insurance. The document also provides examples of insurers that are leveraging telematics data and innovative technologies to improve their business operations and customer experience.

1. Big Data
an actuarial perspective
Decavi-KPMG,18/11/2015
MateuszMaj
Chairmanof IABE
BigDataWG
mat@motosmarty.com

2. What is Big Data?

3. Internet of Things

4. What is Big Data?

5. What is Big Data?

6. What is Big Data?
source:www.hp.com

7. Why Big Data
Working Group?
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 through CPD
courses

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. Pricing
Usage-BasedInsurance(UBI)
Full
UBI
–
niche
market
segments
Motor
insurance
telema7cs
–
mass
market
Works
well
with
niche
segments:
• Young
drivers
• People
living
in
risky
regions
• Low-­‐mileage
drivers
Insure
the
Box,
UK
New
disrup=ve
model
for
low-­‐mileage
drivers
with
prepaid
miles
(similar
to
prepaid
mobile.
No
punishment
for
bad
driving.
Self-­‐selec=on
+
individualized
premiums.
Progressive,
US
“Prince
of
pricing”
with
MyRate
(pricing
variable
innova=on)
&
Snapshot
(1st
telema=cs-­‐based
insurance)
offers
Self
selec=on
&
individualized
insurance
premiums
Intesa
SanPaulo
Assicura,
IT
Telema=cs
play
key
posi=on
–
car,
bike
home
Created
viable
risk-­‐based
pricing
model
with
phased
approach
for
Viaggia
con
me:
Phase
1
(tradi=onal
variables),
Phase
2
(Banking
parameters),
Phase
3
(Purchase
and
use
variables),
Phase
4
(Behaviour
variables)

18. 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

19. 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
theW
• 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

20. Covers different
Legislation
EU-widelaw
underconstruction

21. Insurance
Canitbesexy?
Health insurance, US
Oscar is a health insurance company that employs technology, design,
and data to humanize health care.
Technology as differentiator (telemedicine) - system connects doctors and
patients with help of mobile app, website, bills, free fitness-tracking devices – easy
to use and understand.

22. Insurance
Canitbesexy?
Home Insurance, IT
Habit@t is the first Connected Home Insurance by Cardiff;
Well targeted customers – young, tech-savvy, from big towns;
IoT based - combining home telematics with home insurance;
Superior customer relationship - user-friendly & seamless usage via
smartphone app, multichannel with strong support;
Proactive alerts and risk managements.

23. EU-widelaw
underconstruction
Role of actuaries
Datascientists

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