The Financial Services Sector Coordinating Council has released the final four of six resources to help the financial services sector safely deploy artificial intelligence.
The resources were developed by the Artificial Intelligence Executive Oversight Group, a private-public partnership that brought together financial institution executives with federal and state regulators and other stakeholders to identify gaps in the financial sector’s use of AI. The first two resources were released in February.
The four new resources are:
- The identity and authentication resources, co-authored by the American Bankers Association, include “Mitigating AI-Powered Attacks Against Identity and Authentication” and associated “Recommendations for Policy Makers.” The mitigating AI-powered attacks deliverable outlines three primary attack vectors comprising 10 specific tactics that threaten identity and authentication systems and mitigation strategies. The paper also includes a maturity model for identity controls to combat malicious use of generative AI.
- “AI and Explainability in Finance: Explainability Challenges, Practices and Recommendations” focuses on generative AI, underscoring the need for continuing collaboration across the sector, with regulators and third-party providers on how financial institutions can fulfill the core objectives of explainability. It also includes steps firms should consider to deliver intended and trustworthy outputs, utilize tools effectively, and apply guidance to enhance explainable AI and ensure transparency.
- The Data Nutrition Labeling (DNL) deliverable, authored collectively by the FSSCC Data Nutrition Labeling Workstream and PNC Financial Services, recommends a structured approach for the evaluation of data quality as it relates to AI solutions in the financial sector, to support increased transparency and trust in the use of AI, and ensure alignment with state, federal, and international regulatory standards and guidance. To further support practical adoption, a companion video has been developed demonstrating the application of the DNL in a real-world context. The video is intended for industry representatives to share within their organizations to illustrate the value of the DNL, accelerate stakeholder understanding, and help build the internal business case for integration into existing data governance and AI risk management practices.
- The AI Enhanced Fraud resource, co-authored by ABA, provides information on the AI Fraud Attack Landscape, details on what education and awareness programs should look like to counter these trends, incident response and operational reporting considerations, including how to respond to deepfakes, controls and technology responses, and a summary of how the ecosystem and sector is coming together to combat these issues together.
The previously released resources are:
- The AI Lexicon defines key AI-related terms based on definitions from various industry standards and government resources with the goal of improving sector communications, on aspects ranging from risk management to contract negotiation.
- The Financial Services AI Risk Management Framework, or AI RMF, is a retooling of the National Institute of Standards and Technology’s AI RMF that is specifically tailored for financial services. The FS AI RMF consists of four parts — an AI adoption stage questionnaire, a risk and control matrix, a user guidebook, and a control objective reference guide.
