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 economical and technological revolution… …being defensive is a waste of time as it is unavoidable and lethal ” - Henri de Castries AXA CEO

Main Big Data business initiatives and solutions Acquisition Customer value Claims cost control UW & Pricing Breaking new insurance grounds 3 | 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 4 | SMART DATA AND DATA INNOVATION LAB

The emergence of data science team Project manager Legal officer Big Data system SOFTWARE engineer ENGINEER AXA Information System SMART DATA AND DATA INNOVATION LAB

Is privacy (and ethic) becoming a luxury good? (from London Strata 2015) Compliance AXA.COM Commitment to transparency Why data privacy matters for AXA? Safeguard personal data AXA's Data Privacy Declaration Use of Personal Data Dialogue and Transparency AXA’s Data Privacy Advisory Panel Compliance is at the core of our incubation process Da Data Priv rivacy Binding Corporate Rules Anonymization process Fr Framework Data processing agreement Encryption Data retention and life cycle IT architecture management – GDPR Security test compliance Privacy impact assessment Data residency policy 6 | Big Data update

Is privacy (and ethic) becoming a luxury good? Ethic Contextualization and transparency Privacy & inference Intrusive approach End of Mutualisation ? Exclusion & non explicit Discrimination 7 | Big Data update

Learning in the data cube* > An industry perspective Biased Rare Imbalanced Noisy X o X o X o n observations Labels Biased Redundancy k actions Growing volume Real-time Low Meta data management Maturity d dimensions Personalized treatment learning (causal inference) Not randomized treatment Acess to data Interpretability Data quality (format, missing Reality data, noise…) Performance monitoring and causality Historic duration (e.g. homophily vs influence, true lift) Unstructured data Curse of dimensionality (generalization challenge) * From an idea of F. Bach 8 | SMART DATA AND DATA INNOVATION LAB

How to really become data driven? Key challenges to really change the business 9 | SMART DATA AND DATA INNOVATION LAB

THANK YOU! Contacts Philippe.mariejeanne@axa.com