Business
Harnessing Generative AI for Risk and Compliance Management in Asian Banks
This article explores how Asian companies can capture generative AI’s potential value through organizational change, upskilling, and focusing on specific use cases.
TL;DR: Generative AI in Asian banks
- 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.
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.
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.
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.
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.
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