big data - an actuarial perspective
TRANSCRIPT
Mateusz Maj Chairman of IABE
Big Data WG [email protected]
Big Data an actuarial perspective
1st IABE Big Data Forum
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 !!!
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.
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.
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
• 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 making insurance sexy
Pricing Rating trends
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?
Univariate
basis
Risk
modelling
Technical
premium
modelling
Scenario
testing
Price
optimisation
Extra data
sources
Telematics
data?
Pricing Rating trends
• New rating factors;
• Flexible, dynamic risk pricing;
• New modelling techniques like machine learning;
• New, disruptive insurance offerings like Usage-
Based Insurance.
Pricing
Pricing Usage-Based Insurance (UBI)
UBI is the scheme where insurance premiums are
calculated based on dynamic causal data, including
actual usage and riskier driving behavior.
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
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
Why Change?
- Expensive customer acquisition
- Little contact with customer
- Low brand loyalty and retention
- Regulatory pressure …
Insurance Can it be sexy?
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