In a context where technological innovation is redefining standards of performance and efficiency, BNP Paribas' "AI for Efficiency" program, developed in collaboration with Artefact, stands out as an ambitious model for transforming commercial banking through artificial intelligence.

This project demonstrates how a structured, value-driven approach can not only optimize processes but also address the challenges of an ever-evolving industry.

Transforming Commercial Banking: AI for Efficiency

BNP Paribas’ “AI for Efficiency” program, developed with Artefact, aims to transform commercial banking through AI. By focusing on performance, efficiency, and value creation, this project embodies an ambitious and structured vision of AI integration into banking processes.

Program Structure: A Comprehensive and Pragmatic Approach

Launched in six months, “AI for Efficiency” is built on three key pillars: value, industrialization, and risk management. These focus areas ensure the relevant use of AI while adhering to regulations and creating a lasting positive impact. A “Factory” responsible for industrialization guarantees the sustainability of innovations, while a center of excellence drives the program through four key areas: expertise, value, exploration, and coverage. These areas foster innovation while maintaining consistency between applied research, risk management, and technological adaptation.

Use Cases: Concrete Examples of AI Impact

The program includes several AI application streams:

  • Fraud: Machine learning algorithms analyze customer behaviors to identify anomalies and prevent various types of fraud, such as suspicious transfers.

  • Document Processing: OCR (Optical Character Recognition) and NLP accelerate the processing of customer files, improving service speed and quality, especially for mortgage loans.

  • Generative Assistants: These tools offer natural language information retrieval solutions for both customers and employees, facilitating interactions and decision-making.

  • Marketing and Interactions: Predictive models optimize marketing campaigns and response effectiveness to customer inquiries, ensuring a more personalized and relevant service.

“We led the project because it creates value, estimated and endorsed by the business units, departments, and validated by AI experts.”
Jérémie CORNET VUCKOVIC, Consulting Director at Artefact

Key Success Factors: Value, Industrialization, and Accountability

  • 1. Focus on Value: Each project is evaluated based on its direct impact on the bank and its clients. This ensures strategic resource allocation and optimal solution adoption.

  • 2. Industrialization: The establishment of a Factory ensures that developed solutions move beyond the demonstration stage and are sustainably integrated into banking processes.

Challenges and Next Steps: The Stakes of Sustainable Transformation

  • Rapid Technological Evolution: Generative AI and multimodal models offer promising prospects but require constant monitoring to stay at the forefront

  • Carbon Footprint and Energy Costs: Reducing the environmental impact of AI models is a priority, notably through partnerships with companies like Mistral.

  • Skill Development: Training and retaining talent is crucial to maintaining high levels of expertise and innovation.

  • Risk Management: Special attention is given to fairness, transparency, and compliance in the deployed solutions.

“For this initiative to endure over the next two years, it must become industrialized. It needs to be fully integrated into the bank’s or the company’s processes.”
Adrien VESTEGHEM, AI Program Director at BNP Paribas