Generative AI (gen AI) can revolutionise risk management in banks, leading to a "shift left" approach and improved efficiency.,Gen AI applications in risk and compliance include regulatory compliance, financial crime, credit risk, and climate risk.,Responsible gen AI adoption requires banks to address new risks, data and tech demands, and talent and operating-model requirements.
Introduction
In the rapidly evolving world of financial services, generative artificial intelligence (gen AI) is set to revolutionise risk management and compliance in Asian banks. By automating, accelerating, and enhancing various tasks, gen AI can significantly improve efficiency and effectiveness.
The Promise of Gen AI
Gen AI holds immense potential for transforming risk management in banks over the next three to five years. This shift will allow risk professionals to focus on strategic risk prevention and partner with business lines, leading to a "shift left" approach. This transition will free up resources for exploring emerging risk trends, strengthening resilience, and improving risk and control processes proactively.
AI-Powered Risk Intelligence Centres
The advent of gen AI could lead to the creation of AI- and gen-AI-powered risk intelligence centres. These centres would serve all lines of defence, providing automated reporting, improved risk transparency, and higher efficiency in risk-related decision-making.
For instance, McKinsey has developed a gen AI virtual expert that provides tailored answers based on proprietary information. Similarly, banks can develop tools that scan transactions, potential red flags, market news, and asset prices to influence risk decisions.
Gen AI Applications in Risk and Compliance
Gen AI has promising applications in various areas of risk and compliance, including regulatory compliance, financial crime, credit risk, modeling and data analytics, cyber risk, and climate risk. These applications can be categorised into three use case archetypes: virtual experts, manual process automation, and code acceleration. For more on the broader impact of AI in the region, explore APAC AI in 2026: 4 Trends You Need To Know.
Key Considerations for Gen AI Adoption
While gen AI offers numerous benefits, prioritising use cases is critical for responsible and sustainable adoption. Chief risk officers should base their decisions on assessments of impact, risk, and feasibility, aligning with their banks' overall visions and relevant regulations. This focus on structured governance is also seen in North Asia: Diverse Models of Structured Governance.
Winning Strategies for Gen AI Journey
Organisations should adopt a focused, top-down approach to start their gen AI journey. They should begin with three to five high-priority risk and compliance use cases that align with their strategic priorities, scaling the applications through the development of a gen AI ecosystem. The integration of AI into business operations is a growing trend, as discussed in Executives tread carefully on generative AI adoption.
Navigating New Risks and Requirements
Responsible gen AI adoption requires banks to understand and address new risks, data and tech demands, talent, and operating-model requirements. This includes evolving risk mitigation capabilities, ensuring data accuracy, and training teams on gen AI's limitations and strengths. This approach aligns with the principles of Why ProSocial AI Is The New ESG.
Comment and Share
How is your organisation preparing for the shift towards generative AI in risk and compliance management? Share your thoughts and challenges in the comments below.
Latest Comments (5)
@chenming: it's good to see McKinsey pushing their "virtual expert" but realistically the big Chinese banks are already building out very bespoke internal gen AI solutions for risk. they aren't waiting for external consultants. they have the talent and data in house to do it themselves.
The McKinsey "gen AI virtual expert" sounds interesting, reminds me of how we're building our tutoring LLM. It's all about fine-tuning on proprietary info to get those really tailored, accurate responses. That's where the real value is for specific industry applications, not just general chat.
The "shift left" approach they mention for risk professionals focusing on strategic prevention is something we've been trying to implement here in London for a while now. Getting the traditional risk teams to pivot from reactive to genuinely proactive, even with these gen AI tools, is proving to be a rather tricky cultural hurdle. It's not just the tech, is it?
McK's gen AI virtual expert, scanning financial data, is similar to how we validate assembly line protocols. Input data accuracy is critical; garbage in, garbage out.
The McKinsey virtual expert sounds like powerful tool for banks. My question is, how does this integrate with the existing IT infrastructure in a typical Asian bank? So many legacy systems. Is it all cloud-based or can some parts run on-premise for data privacy reasons? This is big concern for our clients.
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