Webinar on AI Risk Management Framework for Banking held on 27 August 2026

The webinar featuried two True North Partners, Wolfram Hedrich and Vikas Deep Sharma, with Deputy Secretary-Treasurer of the Asian Bankers Association, Mig Moreno serving as Moderator. The webinar was a tremendous success measured by the 560 registrants from 35 countries, the numerous questions during the Q&A session and the positive comments, and the Thank-you notes received from high-ranking bankers. Hereunder is the summary of the presentations made during the webinar. In his opening remarks, Moreno highlighted the rapid expansion of artificial intelligence across banking, from customer service to more advanced applications involving generative and Agentic AI. As AI penetrates deeper into financial institutions, he stressed that risk management becomes increasingly important given the inherently risk-sensitive nature of banking. Hedrich began by examining the rapid development of AI within financial services. Banks are experimenting with technologies ranging from machine learning to large language models and Agentic AI, but actual deployment remains more limited than experimentation. Regulatory compliance, data privacy, data quality and implementation concerns continue to slow the transition from prototypes to production. While generative AI makes it relatively easy to create new tools, deploying them safely and securely within a bank is much more difficult. AI use is already expanding beyond chatbots into fraud detection, anti-money laundering, loan processing and other banking functions. Hedrich noted that many institutions believe they should have established stronger governance frameworks and engaged boards, management and regulators much earlier. Sharma then outlined how banks can convert these risks into a practical governance framework. Most institutions begin by strengthening existing model-risk, cybersecurity, technology-risk and third-party-risk structures because governance and accountability are already in place. However, some banks are moving toward more dedicated AI governance arrangements as they recognize that AI risks can emerge faster and in different ways. Regulatory approaches also vary: some jurisdictions impose explicit restrictions, while Asian regulators generally emphasize principles rather than providing a complete implementation blueprint. Banks therefore need to translate regulatory principles into practical and enforceable controls. A strong framework should include an AI-specific risk appetite, enhanced model-risk management, appropriate vendor-selection processes and stronger organizational capabilities. Banks should maintain inventories not only of AI models but also of AI use cases and systems using LLMs, with lifecycle controls covering acquisition, development, implementation, monitoring and change management. Traditional validation and testing approaches must also evolve because explainability and risk tiering are significantly more complex with generative AI. Four Key Takeaways 1. AI requires stronger and updated banking risk frameworks. Traditional model, cyber and technology controls remain relevant, but generative and Agentic AI introduce new risks that require additional safeguards. 2. Guardrails must be automated and embedded throughout the AI lifecycle. “Policy as code” allows banks to translate risk requirements into enforceable technological controls rather than relying mainly on documentation or manual review. 3. AI governance is an institution-wide responsibility. Risk, IT, cybersecurity, business units and senior management must operate under clear accountability and coordinated governance. 4. Strong controls enable, rather than prevent, AI innovation. Effective inventories, risk tiers, automated monitoring, kill switches and controlled experimentation give banks the confidence to expand AI adoption safely.