HOW BIG IS BIG DATA FOR AN INSURER LIKE AXA? CHALLENGES & - - PowerPoint PPT Presentation

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HOW BIG IS BIG DATA FOR AN INSURER LIKE AXA? CHALLENGES & - - PowerPoint PPT Presentation

HOW BIG IS BIG DATA FOR AN INSURER LIKE AXA? CHALLENGES & OPPORTUNITIES Paris Big Data Management summit 24 nd March 216 Philippe Marie-Jeanne Group CDO & Head of the Data Innovation Lab Philippe.mariejeanne@axa.com Big Data is an


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HOW BIG IS BIG DATA FOR AN INSURER LIKE AXA?

CHALLENGES & OPPORTUNITIES

Paris Big Data Management summit 24nd March 216

Philippe.mariejeanne@axa.com Philippe Marie-Jeanne Group CDO & Head of the Data Innovation Lab

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”Big Data is an economical and

technological revolution… …being defensive is a waste of time as it is unavoidable and lethal”

  • Henri de Castries

AXA CEO

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3 | SMART DATA AND DATA INNOVATION LAB

Main Big Data business initiatives and solutions

Acquisition Customer value Claims cost control UW & Pricing Breaking new insurance grounds

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4 | SMART DATA AND DATA INNOVATION LAB

The Data Innovation Lab as a transformation engine within AXA

AN INTERNATIONAL TALENT POOL SPECIFIC METHODOLOGIES DATA! A TEAM OF SELECTED EXPERTS PLATFORMS & TOOLS

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AXA Information System

SOFTWARE ENGINEER

The emergence of data science team

SMART DATA AND DATA INNOVATION LAB

Big Data system engineer Project manager Legal officer

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Is privacy (and ethic) becoming a luxury good? (from London Strata 2015)

6 | Big Data update

Compliance AXA.COM Commitment to transparency

Why data privacy matters for AXA? AXA's Data Privacy Declaration AXA’s Data Privacy Advisory Panel Safeguard personal data Use of Personal Data Dialogue and Transparency

Da Data Priv rivacy Fr Framework

Binding Corporate Rules Data processing agreement Data retention and life cycle management –GDPR compliance Data residency policy

Compliance is at the core of our incubation process

IT architecture Anonymization process Encryption Privacy impact assessment Security test

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Is privacy (and ethic) becoming a luxury good?

7 | Big Data update

Ethic Contextualization and transparency Privacy & inference Intrusive approach Exclusion & non explicit Discrimination End of Mutualisation ?

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8 | SMART DATA AND DATA INNOVATION LAB

Learning in the data cube*

> An industry perspective

n observations d dimensions

* From an idea of F. Bach Biased Redundancy Growing volume Real-time Low Meta data management Maturity Acess to data Data quality (format, missing data, noise…) Historic duration Unstructured data Curse of dimensionality (generalization challenge) Biased Rare Imbalanced Noisy

Labels

X X X o

  • Personalized treatment learning (causal

inference) Not randomized treatment Interpretability Reality Performance monitoring and causality (e.g. homophily vs influence, true lift)

k actions

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How to really become data driven?

9 | SMART DATA AND DATA INNOVATION LAB

Key challenges to really change the business

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THANK YOU!

Philippe.mariejeanne@axa.com Contacts