axa – a global company - industrial engineering and ... · data innovation lab is missioned to...
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About AXA
Engineering LabSwitzerlandAXA Lab
San Francisco
Data Innovation LabSuresnes, Paris
Data Innovation Lab PlatformAtlanta
Data Innovation LabSingapore
Digital AgencyParis
AXA Strategic Ventures New York
AXA Strategic Ventures Paris
AXA Strategic Ventures London
AXA Strategic Ventures San Francisco
AXA Strategic Ventures Paris, Berlin, Zurich
AXA Strategic Ventures Hong Kong
Data Innovation Lab is missioned to transform AXA Group into a data driven company,
developing a significant share of business (product, services, tools…) from the proactive
use of data for new usages
AXA LabShanghai
Data Innovation Lab Bangalore
Innovation Ecosystem at AXA
#1 Global Insurance Brand for 7 consecutive years1 |
Worldwide, AXA redefines standards to better care about people (employees, customers and communities)
AXA is a leading global company in insurance and asset management
AXA creates innovative products and better quality of service
AXA promotes an open working environment
AXA provides challenging and empowering jobs with strong expertise and learning opportunities
Global Insurance Brand 2015 Top 100 Global Brand
Global Insurance Brand 2015 Top 100 Global Brand
#1
* Global ranking by Interbrand (brand strategy and design consultancy)
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How Big is Big Data for AXA
by Philippe Marie-Jeanne AXA Global Leader of Data Innovation Lab
Data Innovation Lab|
Bio Data
Before being Head of the Data Innovation Lab (Axa Group) that he created on
January 2014, Philippe Marie-Jeanne was Group Head of P&C Retail since 2011
Before joining AXA Global P&C, Philippe Marie-Jeanne was since February 2007
P&C Technical Head at AXA France
From 1998 to 2007, Philippe hold different positions at the SMABTP, leading
insurer in the building and construction sector, as Deputy General Manager in
charge of technical direction for reinsurance and major accounts. He was also a
member of Covéa Fleet Executive Board and has been CGI Bat Vice-Chairman
(bonds business in the construction area)
From 2000 to 2011, Philippe Marie-Jeanne has been a member of the CPABR
(FFSA Plenary Commission). He has also been President of the FFSA Statistician
Committee from September 2008 to March 2011
Philippe started his career in 1989 at UAP Incendie et Accidents and hold
different positions until 1997, when he joined Tillinghast – Towers Perrin as
Senior Consultant
Philippe Marie-Jeanne reports directly to Veronique Weill, AXA Group COO
Philippe Marie-Jeanne is a graduate engineer of the Polytechnique School, but
also graduated from the National school of Statistic and Economic
Administration (ENSAE) and member of the French Actuary Institute (IAF). He
also follow an INSEAD executive program in 2006 (Programme supérieur pour
Dirigeants)
AbstractAt AXA, we have a strong belief that data will reshape our
industry. What does Big Data mean for AXA, what are our main
challenges to create value from data ? The Data Innovation Lab
has been created in January 2014 in order to support AXA’s
transformation into becoming a data driven company
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Topics of Business InterestData Innovation Lab|
Objective
AXA Data Innovation Lab’s key objective is assimilate huge volumes & variety of
data from all possible data sources and use advanced data-driven solutions and
technology to tackle insurance business problems
Topics of projects / research (done or in progress):
• Customer behavior modeling (targeted marketing, behavioral risk predictors,
customer orientation, etc.)
• Predicting & managing insurance fraud
• Increase market share through cross-sell, up-sell, lead management and
conversion
• Use of connected devices and apps (e.g., telematics, eHealth, etc.) for
insurance risk pricing
• Use of digital / social media / other non-traditional data for insurance business
(e.g., customer sentiment analysis for predicting lapse)
• Advanced predictive modeling for underwriting, customer retention, business
acquisition
Other research areas in focus:
• Feature engineering to determine predictive power of different data types for
different insurance business needs
• Tools to separate relevant data from big data to avoid information overload
• Approaches to make the analytics and data modeling more agile for faster
implementation
• Real-time underwriting decisioning at point of sale
• Application of machine learning & clustering techniques to different areas of
insurance business