Banks Can Slash Production Time by Up to 60% with GenAI, HKMA Report Reveals

Data Pre-processing

Once datasets were collected, they required pre-processing before being used to improve data quality and protect privacy.

To maintain consistency and support efficient fine-tuning, banks carried out several data-cleansing and standardisation steps. A key objective was to remove irrelevant “noise” from text inputs and ensure more uniform model outputs.

Banks also implemented several techniques to remove or obscure sensitive information. This included data masking, tokenisation in a data-security context, synthetic data generation, and pseudonymisation.

Beyond structured data, banks also worked with large amounts of unstructured content. Document pre-processing remains important, even for multimodal LLMs.

As LLMs also have a limited context window, long documents must be split into smaller, logically structured segments. Data chunking helped in preserving context, according to the report.

Data Augmentation

To reduce the risk of overfitting and minimise hallucinations arising from limited training samples, select banks used data augmentation to expand and enrich their datasets. Another method focused on synthesising user inputs.

Banks also paid close attention to multilingual balance. Tests confirmed that balancing English, Traditional Chinese, and Simplified Chinese is critical to prevent data skew and ensure terminological accuracy across language variants.

Data Quality Check

A thorough data quality check is vital to assess the reliability of the data assets and uncover any underlying issues.

Handling missing data was another important part of the process.

This required identifying which fields were optional or frequently absent, then deciding on the most appropriate approach: whether to impute the missing values, label them as “Unknown,” or remove non-essential fields that were consistently incomplete.

The final step was comprehensive documentation, which ensured the entire process could be reproduced and provided a clear reference for future updates.

Continuous Data Updates

Maintaining model accuracy requires a continuous flow of fresh, relevant data. This would involve implementing automated or semi-automated data pipelines designed to ingest new information from approved sources.

Once collected, this incoming data was processed using the same cleansing and standardisation procedures applied during the initial pre-processing stage, ensuring consistency across the dataset.

With refreshed and expanded datasets in place, periodic model retraining would help prevent performance degradation over time and ensure the model stayed aligned with evolving language patterns, product updates, and current market conditions.

Strong Early Results, With Guardrails Still Essential

The first cohort of the HKMA GenAI Sandbox delivered clear, tangible gains for participating banks.

Fine-tuned LLMs were also able to analyse 100% of case narratives, far surpassing traditional sampling methods. User response was consistently positive as well, with 86% of GenAI outputs rated favourably and more than 70% of GenAI-generated credit assessments considered valuable references.

At the same time, the sandbox’s report indicated that risks still must be managed. Hallucinations and factual inaccuracies remain key concerns in high-stakes banking environments.

While advanced prompt-engineering techniques helped reduce these errors, banks recognised that these methods mitigate rather than eliminate the issue. For sensitive applications, additional safeguards like RAG and fine-tuning are still required to ensure accuracy and reliability.

Key priorities for the second cohort include “embedding AI governance within three lines of defence, developing ‘AI vs AI’ frameworks for proactive risk management, and implementing adaptive guardrails and self-correcting systems”.

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