On 31 Oct 2025, the HKMA published practical insights from its inaugural GenAI Sandbox, demonstrating that structured data strategy, PEFT optimisation, and multi-layered risk mitigation (hallucination, bias, security) enable banks to achieve 30-80% efficiency gains in risk management and customer service. The report confirms GenAI's operational viability without proposing new regulations, guiding future adoption through validated technical frameworks.
This article was generated using SAMS, an AI technology by Timothy Loh LLP.
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Introduction and Purpose of the GenAI Sandbox
On 31 Oct 2025, the Hong Kong Monetary Authority (HKMA) published the 'Responsible Innovation with GenAI in the Banking Industry' Practical Insights Report, detailing findings from its inaugural GenAI Sandbox initiative. The report outlines practical insights gained by participating banks through a risk-controlled environment for developing and testing GenAI solutions across risk management, anti-fraud measures, and customer experience domains, without proposing new regulatory requirements.
Key Technical Insights and Implementation Framework
The report establishes a foundational framework for GenAI adoption in banking, emphasizing data strategy as critical to model performance. It details best practices for data collection, pre-processing (including linguistic alignment, privacy protection via anonymisation and synthetic data), augmentation techniques, and continuous data updates. Participating banks demonstrated that structured dataset partitioning (70-20-10 training/validation/test splits) and rigorous data quality checks significantly enhance model reliability and reduce hallucination risks.
Optimisation Strategies and Risk Mitigation
The report identifies three core optimisation strategies: Prompt Engineering (including zero-shot, few-shot, and chain-of-thought techniques), Retrieval-Augmented Generation (RAG) for grounding outputs in external knowledge, and Parameter-Efficient Fine-Tuning (PEFT) using LoRA/QLORA to achieve cost-efficient model adaptation. For risk mitigation, it details practical approaches to address hallucination (via structured output mandates, contextual bounding, and inference parameter tuning), bias (through neutral persona prompting and data-level diversity), and security (prompt injection controls and sensitive data tokenisation).
Business Outcomes and Future Direction
The inaugural cohort demonstrated tangible business benefits, including 30-80% time reduction in Suspicious Transaction Report preparation, 60% faster document processing, and 86% user satisfaction with GenAI outputs. The report concludes that domain-specific fine-tuning of Small Language Models (SLMs) and iterative validation frameworks deliver optimal cost-performance balance. The second cohort will advance 'AI vs AI' paradigms for proactive risk management and adaptive governance.
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