Keeping BNP Paribas Cardif compliant

Faster, more accurate on-premises processing

With customers in 30 countries and products spanning automotive, telecoms, and finance, BNP Paribas Cardif must take a proactive approach to research and development – especially when it comes to AI use cases.

Keeping BNP Paribas Cardif compliant

Challenge

Cardif's insurance workflows handle everything from ID cards to injury claim reports – vast numbers of varied, sensitive documents. It needed an on-premises solution to process these documents quickly and at scale to keep data safe, outpace competitors, and protect its brand reputation.

Solution

Using Domino as its in-house platform, Cardif built an intelligent document processing system that delivers:

Modular, future-proof architecture
Modular, future-proof architecture

The team can decouple IT services (like security and data storage) from AI infrastructure, letting them deploy new models without having to rewrite loads of code.

Compliant, on-premises processes
Compliant, on-premises processes

AI models can be packaged into portable formats like ONNX and run entirely on-premises, helping Cardif meet strict data-protection regulations across markets.

Improved model accuracy
Improved model accuracy

Its generative AI models can scan documents without relying on Optical Character Recognition (OCR) or tedious, error-prone manual annotation.

 

The pipeline-first, on-premises approach helps speed up AI innovation, reduces operational costs by minimizing memory requirements, and improves model responsiveness with lower latency.

Results

Powered by Domino, Cardif’s in-house AI models now achieve 85% accuracy, significantly outperforming third-party tools. It can also keep critical customer data on-premises for added security, and rapidly roll out new AI capabilities to better serve its customers.

Generative AI models

with 85% extraction accuracy

On-premises deployments

enabling secure, compliant processes

New use cases built on

end-to-end generative pipelines

When we benchmark what we can do on-premises compared to external vendors, we do far better.

— Sébastien Conort, Chief Data Scientist & ML Engineering Director, BNP Paribas Cardif.

Source: Building future-proof in-house AI systems: An illustration with intelligent document processing session delivered at RevX London.

View more case studies

Domino Data Lab, Inc. Made in San Francisco.

Visit Domino Data Lab homepage