From providing customer service via chatbot to answering queries alongside search engine results, artificial intelligence (AI) tools are becoming seamlessly integrated into daily online activities. However, when it comes to deploying AI at the organizational level—particularly in highly regulated industries like banking and financial services—business leaders must prioritize compliance throughout the process. AI models and internal knowledge management protocols should align with each organization’s established risk management and security standards.
Whether developing proprietary AI models or customizing existing products to meet specific needs, it’s essential to focus on compliance at every stage. The following best practices will help ensure that compliance concerns and requirements are integrated into your organization’s AI tools and systems from the beginning.
1. Establish a baseline AI compliance policy
Banks and financial institutions must follow stringent requirements around data protection and security, and AI programs are no exception. Before adopting AI, organizations need to establish a baseline compliance policy that addresses data privacy, ethical considerations, and the protection of sensitive information. In addition to providing clear guidelines for internal teams, the AI compliance policy should serve as the foundation for ongoing governance as technology evolves and additional AI use cases are explored.
2. Create an AI governance committee with compliance representation
In addition to establishing a baseline AI compliance policy, organizations would benefit from an AI governance committee that includes key stakeholders from security, legal, product development, and compliance teams. Different perspectives are valuable when implementing new technology across departments, and having representation from a variety of internal teams can help avoid or address challenges that may arise within individual verticals. Banking and financial services companies should make sure this committee includes a representative from the compliance department.
Additionally, inviting senior leadership to participate in the AI governance committee can help ensure a strategic approach to managing compliance needs, as well as lay the groundwork for effective internal communication when implementing AI tools and policies throughout the organization.
3. Evaluate acceptable risk levels and data processing compliance
Risk management is a vital function in banking and financial services, and each wave of technological innovation–such as digital banking–has required institutions to revisit and refine their risk protocols. When implementing AI, companies in highly regulated industries need to involve risk management teams and consult the AI governance committee mentioned above in determining the acceptable levels of risk associated with AI deployment, and ensure that any data processed through AI tools complies with industry-specific regulations. Established risk management considerations at banks and credit unions should serve as the basis for internal compliance policies to mitigate risk and avoid non-compliant uses of AI. Safeguarding sensitive information in source systems may require creative strategies, such as cleansing potentially sensitive numerical data (e.g., account numbers) prior to export or implementing programmatic filtering within AI data connectors.
4. Build or use a customized AI model for compliance tracking
Developing a centralized knowledge base to manage compliance documentation, AI policies, and vendor agreements can help avoid internal inefficiencies and miscommunication, which is critical to developing a successful AI program. A solid knowledge management framework also promotes compliance throughout the organization and provides a basis for regular audits and continuous improvement.
Business leaders in highly regulated industries should consider investing in a proprietary LLM or customizing a generative AI model like ChatGPT for knowledge management. Regardless of the platform used, relevant teams need access while enabling all AI-related activities to be auditable and transparent. A generative AI chatbot can make it easier for team members to locate accurate information quickly.
Implementing AI tools at the enterprise level requires a framework for success, and in sectors with strict regulatory oversight like financial services, layering on compliance requires strategic planning and execution.
Organizations adopting AI under regulatory restrictions should start by establishing a baseline AI compliance policy. From there, they should create an AI governance committee that includes compliance representation, ensuring oversight is built into decision-making from the outset.
It’s also essential to evaluate risk and data processing workflows in partnership with existing risk management teams. Additionally, developing a centralized knowledge base to track compliance-related documentation, policies, and agreements ensures transparency and continuity.
By integrating compliance into every stage of AI implementation and evolution, organizations can more confidently harness the potential of AI while maintaining regulatory adherence and operational efficiency.
