AG2R : projects and Machine Learning

AG2R La Mondiale is a French organization offering a wide range of insurance products to 15 million individual and 500,000 corporate clients.

Cover Cas client AG2R

Challenges

The group wanted to develop new projects based on Machine Learning , but faced several challenges:

Strong Regulatory Constraints:

Scoring Action Imperative

Strong regulatory pressure on insurers to combat money laundering and terrorist financing, forcing industry players to invest heavily in scoring models using supervised learning technologies. 

Transition to Production:

Challenges for ML Initiatives

Several Machine Learning initiatives had been launched internally using a mainly build-based approach, but these struggled to scale up and did not reach the production phases.

Absent Data Organization :

Lack of Tools and Methods

The company often lacked the technological tools and methods to operate them necessary to create, monitor and maintain these models. 

Solutions

The DataOps Platform was chosen by AG2R La Mondiale to develop and supervise its scoring models.

Scores factory:

Technical Foundation for ML Initiatives

Created a score factory placing Saagie as the technical foundation on which the group’s Machine Learning initiatives are built, starting with regulatory compliance use cases and extending to marketing needs.¬†

Enhanced MLOps culture:

Automated supervision

The implementation of MLOps tools and methodologies to automate the supervision of models in production and considerably accelerate the start-up of new projects, from an average of 2 weeks to a few minutes with Saagie.

Strategic co-innovation:

New MLOps functionalities

The adoption of an approach based on co-innovation between AG2R La Mondiale and Saagie’s Professional Services teams to develop new MLOps functionalities to be integrated within Saagie.¬†

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