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Allianz and big data

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by

Vivian Cheng

on 14 December 2014

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Transcript of Allianz and big data

Business Model Canvas
TELEMATICS
TECHNOLOGY

Big Data + Technology
New Business Operation of
Customer Segment
Introduction
Key challenges
BACKGROUND
AXA VS ALLIANZ
Allianz improvement
Allianz will hold customer data in a centralized single customer view (SCV), that was designed and built by Celerity.

BIG DATA &

(AXA, the largest insurance company in Europe)
What AXA has already used with big data
AXA’s data enrichment programe
AXA has further enhanced its pricing capabilities in a deal with Callcredit Information Group, which will provide the insurer with greater access to credit checking and behavioral data tools.

AXA corporate with Endcustomer.co.uk to creates broker ‘big data’ tool
To provide greater customer insight and market competitiveness, which can give them greater insight into existing and potential clients. Allowing them to tailor both the approach and the insurance solution for that client
What is big data?
... the ability to process and analyze large amounts of varied data and data sources together to generate actionable business insights
What are the possible benefits of
integrating big data?
enhances revenues
improves competitive advantage
makes the company more efficient
lowers risk and costs
fuels innovation
acquires new customers
25%
of insurers are investing in big data projects in 2014: increase from
9%
in 2012
potential for big data can reach up to
2%
of premiums in insurance areas
The experience of early adopters
Allianz's deal with digital marketing software company Neolane
Allianz introduced a worldwide standardized IT infrastructure
Allianz has invested in a shared service center, shared platforms and shared applications in business intelligence and business analytics solutions to manages real-time awareness of the customer and market conditions.
one of the largest insurers in the UK - Allianz ranks 2nd in Europe
offers commercial insurance
provides a wide range of personal lines products
Data required
Reducing fraud

Customizing premium rates
Social media and internal data sources
Geographical location
Contact information
IP addresses
Criminal records
Mileage, speed, cornering, harsh braking,frequent lane changing, acceleration and braking trends
Time of day or night on the road
Weight detection
G-forces

Tools and technologies needed
Customizing premium rates
Reducing Fraud
Benchmarking facilities , blacklisting and monitoring facilities to do real-time analytics of social media
Image recognition and processing
Handwriting scanner
Data visualizations
Human skill

Big Data Analysis
Impacts of Big Data on
Big Data Analytic platform
Adache Hadoop
Machine learning, data fusion, data mining, time series analysis, visualization
Use of mapping layers to pull in multiple data streams, either internally or externally, into a consolidated view

Improve customer service
Improve advertising
Claims mitigation and prevention
Predicting future diseases and climate changes
Reducing fraud
Customizing premiums

Why focus on reducing fraud?
Why focus on customizing premiums?
in 2013, UK insurers detected
495
fraudulent applications for motor insurance per day
more than
£2.1bn
undetected insurance fraud is committed every year

CONSEQUENCE :
adding an average of £50 to the annual premium costs for individuals

OVERALL:
cost-saving for both Allianz and customer + reduce time taken claim processing
Snapshots
GPS device
Palm size tracker under the seat
3D camera
Cloud -based storage

Developing new product
inexperienced drivers
good drivers
Value Proposition
Key resources
Cost
ALLIANZ'S STRATEGY:
develop tailor-made products for specific customer groups and circumstances

Customizing premiums is therefore aligned with Allianz’s current strategy


ALSO: it would reduce costs for the people that are less exposed to risk
Key partnerships
digital labs
IT experts
Reliable data
+ focus on automobile industry
Why?
Features of the product:
enables to be rapidly helped in case of breakdown or accident
provides driving indicators
provides advice for eco-driving
provides a service « Find my car »

Allianz collects data on the driver’s behaviour + encourages him to drive well, to be eco-friendly and to drive safe.

The device then processes the data over a cellular connection, and a clear portrait of individualized driving distance and behavior emerges, making it possible to offer segment-of-one insurance coverage.
We have many partnerships in the automobile industry

19%
of the frauds in the US are in the vehicle sector + car insurance is an important sector of Allianz
BOX telematics limited
Overall process of Big Data
analysis
highly invest on data storage and management
price of technologies
enables to be rapidly helped in case of breakdown or accident
provides driving indicators
provides advice for eco-driving
provides a service « Find my car »
Example:
Collect customer's IP address
RULE:
If the same IP address reports two claims or more in a period inferior to a month, the customer should be put into the group "fraudulent behavior"
Do this for each data collected and create customer's personal profile > determine whether he is fraudulent or not
Use of the common fraud database provided by Insurance Information Centre
Partnerships
Customer privacy
data AND customers AND "insurance" AND ("complain" OR "upset" OR "disturb" OR "don't care")

tools technology
partnerships
Ignored factors
Where to find the data?
Is it reliable?
Third party data = to improve its ability to rate and price risk in a more nuanced way
BUT third party data = not good enough, not reliable

Fraud detection systems become
worthless
when errors are introduced, Allianz is currently using third party data but should make sure there are no errors

What about the future?
Hyundai Marine & Fire Insurance Company lowered its fraud rate by
20%

estimated
2 to 3 %
reduction in auto claims payouts by improving fraudulent-claims detection by around
30%

Conclusion
to customize premiums
Costs
Full transcript