A Strategic Collaboration for Innovation

Celebrating our collaboration with UBS

A Strategic Collaboration for Innovation

IDSIA collaborates with UBS to drive AI innovation in finance. The partnership spans GWM, P&C, Risk, Group Legal, Group Treasury, and more, aligning research with real-world impact.

Group picture celebrating the UBS - IDSIA collaboration

Harnessing AI for Financial Excellence

We combine IDSIA's AI expertise with UBS's leadership to build systems that enhance decision-making, risk management, and operational efficiency.

Progress Through Partnership

Close collaboration between research and industry turns ideas into impact. Our joint work focuses on deployable AI with measurable value.

Selected Projects

In collaboration with UBS, IDSIA advances AI research and development across banking applications. We translate cutting-edge methods into deployable systems.

Focus areas include next-generation risk management, AI-assisted forecasting, and regulatory alignment. We are currently scaling up our collaboration, with a variety of projects being onboarded. At present, we are executing around 10 active initiatives, with nearly 100 people involved across IDSIA and UBS.

Below, we highlight two selected projects.

Ongoing
Risk Governance Framework for Large Language Models
Developing Comprehensive Frameworks for Transparency, Compliance, and Risk Mitigation
About the Project
Our research focuses on the governance of large language models (LLMs) to develop comprehensive frameworks that ensure transparency, mitigate biases, and uphold regulatory compliance. As LLMs become integral to decision-making processes, it is crucial to safeguard against misuse to minimize operational and reputational risks. This project aims to formulate technology and governance strategies that promote the responsible and ethical deployment of AI across various domains.
Ongoing
Knowledge Graphs for AI Performance Enhancement
Exploring Knowledge Graphs to Elevate AI Capabilities
About the Project
Our research investigates the potential of Knowledge Graphs (KGs) to enhance the performance of large language models (LLMs) by incorporating structured, domain-specific data. KGs offer a rich, interconnected representation of concepts, enabling LLMs to provide more accurate context, improve decision-making, and enhance risk assessment. This study seeks to bridge the gap between unstructured text and structured knowledge, thereby making AI-driven insights more reliable and interpretable.

IDSIA

A Strategic Collaboration for Innovation