3 key foundations for implementing AI in financial institutions

This article was originally published on bankdirector.com.

In an evolving technological landscape, the integration of artificial intelligence (AI) presents both opportunities and challenges for financial institutions. Before implementing AI across their operations, financial institutions need three key foundational elements to ensure successful AI adoption and risk mitigation: a clear AI governance framework, strong model risk management and centralized standards.

1. Governance framework

A well-structured AI governance framework must comprehensively address the unique risks and regulatory considerations associated with these advanced technologies. Financial institutions should start with exploratory projects, such as proofs of concept, to gain insights into the operational and risk implications of AI. These insights can then guide the development of an AI governance framework that may either stand as an independent initiative or integrate into existing initiatives in areas such as financial modeling or IT governance.

A financial institution’s AI governance framework should draw upon established industry standards and regulatory guidelines while aligning with the organization’s priorities and risk appetite. More importantly, the framework must include mechanisms for evaluating and prioritizing AI use cases, ensuring alignment with the institution’s strategic objectives and operational requirements.

2. Model risk management 

Experience with financial and risk models provides financial institutions with a foundation upon which to build AI-specific model risk management practices. However, AI technologies, particularly those with autonomous capabilities, require a reassessment of traditional risk management frameworks. Financial institutions must adopt enhanced risk management strategies that account for the unique characteristics of AI models, including the potential for generative AI technologies to produce novel, sometimes unpredictable outputs.

Strategies such as imposing limitations on data inputs and incorporating human oversight of model outputs are essential for mitigating risks and ensuring the long-term reliability and integrity of AI applications.

3. Centralized standards

To balance the need for both innovation and control around AI, financial institutions must develop and enforce centralized standards. These standards should include ethical use policies, technical development guidelines and protocols for AI oversight. Establishing centralized oversight ensures that AI initiatives are adopted and implemented in a consistent and controlled manner, facilitating seamless integration into the institution’s operations and IT environment.

Takeaway

For financial executives, the transition toward AI-enabled operations requires careful planning and the establishment of robust foundations in governance, risk management and standardization. By addressing these critical areas, financial institutions can navigate the complexities of AI adoption, ensuring that these technologies contribute positively to operational efficiency, risk mitigation and overall competitive advantage.