Marketing Personalization
Crédit Agricole Consumer Finance wanted a reusable AI environment that business teams could use to deploy machine learning models for higher-value client targeting, beginning with a use case to optimize LifeTime Value for new clients and recommend the best next offer to sales representatives.
Reported impact
The first full-scale test was described as very conclusive, showing that CEOS can support next-best-offer targeting for new clients while creating a reusable AI platform that can be extended to lending, fraud, risk, and compliance use cases.
Crédit Agricole Consumer Finance built CEOS as an internal data science platform for deploying machine learning models into business workflows. The article says the platform is hosted on the AWS cloud and runs on an open-source Kubernetes foundation, giving the bank a managed environment that can orchestrate deployed applications securely and scale resources up or down automatically with demand. Business data enters the platform from client files, where models learn from customer characteristics to estimate lifetime value and determine the next best offer that representatives can present. Operationally, Digital Services France leads the initiative using an MLOps approach, the Architecture and Innovation team provides business tools and industrialization processes for AI models, the Data Tools team defines the development framework and onboarding path, and CA GIP supplies and maintains the technical base. The same platform is positioned for reuse across lending, fraud, risk, and compliance functions.
Client files are analyzed in CEOS, where machine learning models running on AWS cloud infrastructure and orchestrated with Kubernetes learn customer parameters, estimate lifetime value, and output the next best offer for representatives to present to new clients.
"CEOS is an environment that lets us deploy machine learning models."
"Hosted by the AWS cloud on an open-source Kubernetes foundation"
"The goal? Designing a solution based on artificial intelligence for optimising LifeTime to Value* for new clients."
"This first full scale test has been a very conclusive one, opening up new horizons for more advanced AI usages."
"after analysing the characteristics of our client files, AI is able to target the best offer (NBO) that our representatives can put forward."
"the platform autonomously arbitrates decisions on adding and decommissioning resources depending on requirements. It manages itself."
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
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