Open to All Participants
The power of generative AI, which can enable users to better leverage proprietary and public data and information in use cases that touch everything from trading to preventing financial crime to regulatory reporting to improving customer experiences, will be transformative. Since FINOS launched its broader AI initiative, it was clear that accelerating AI readiness was a critical first step. AI presents new challenges for onboarding technology in financial services that must be addressed to allow for its rapid, safe and trustworthy adoption – building the right guardrails and considering the needs of the customer, the organization and the regulatory environment.
FSIs have a critical need for AI adoption guidelines and frameworks that particularly focus on:
Following months of members-only collaboration, the AI Readiness SIG launched its AI Governance Framework draft at OSFF NY on September 30th, 2024. The Framework is vendor-agnostic and outlines 15 risks and 15 controls specifically tailored for AI systems leveraging LLM paradigms in FS. It is designed to be a 'living document' that addresses current and new threats and builds upon existing risk frameworks.
An invitation to the financial services community to AI Readiness Roadmap
As a critical next step in finalizing the AI Governance Framework and adopting responsible AI practices, FINOS is inviting the wider financial services community to participate in this important collaboration. The task is to develop and operationalize frameworks, policies and tools for the effective, safe, trustworthy and compliant deployment of generative AI technologies. It is critical the framework reflects the diverse needs of the industry and sets the right guardrails for onboarding and operationalizing AI in financial services.
As the group becomes fully open source, firms, academics and practitioners are now able to join community working groups, contribute to the framework’s development and provide feedback to refine and expand the Financial Services AI Readiness reference model through a comprehensive roadmap that seeks to anticipate responsible AI considerations, further use cases and regulatory imperatives.