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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