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Case study: Accelerating compliance transformation with Generative AI

10 July 2025 • 15 mins read

 

In August 2024, Bank of Singapore, in collaboration with OCBC Group Operations & Technology and Group Data Office, embarked on a transformative journey to streamline the Source of Wealth (“SoW”) write-up process for client onboarding and maintenance review. With support from the Monetary Authority of Singapore (“MAS”) Financial Sector Technology and Innovation (“FSTI”) grant, this Proof-of-Concept (“PoC”) leverages Generative AI to enhance risk effectiveness and risk efficiency for SoW writeup.

Background and problem statement

Onboarding high-net-worth individuals in private banking demands a rigorous and highly tailored due diligence process. A critical process component is the preparation of the SoW write-up, which requires clear, accurate and plausible narrative describing our client’s wealth accumulation journey. This complex task is inherently resource-intensive and faces several key challenges, namely:

Compliance with stringent regulatory requirements: Financial institutions must ensure all SoW narratives adhere to strict and evolving regulatory standards, including those set by MAS. Extensive human oversight required to interpret and apply compliance guidelines on verification of documentation, ensuring narratives are accurate and plausible when subjected to scrutiny—adding further complexity and operational burden               .

  1. Manual collation of fragmented data: Relationship Managers (“RMs”) must gather a wide array of documents such as financial statements, sale agreements, inheritance records, and corporate filings from disparate sources. This highly manual collation is time-consuming, and faces risks of human error, data inconsistency, and incomplete information.
  2. Inconsistent RM Write-up: With varying linguistic capability, prior experience and past exposure to different standards, RMs produce SoWs in different format, often inconsistent to bank's standards. This lack of uniformity can lead to confusion and inefficiencies. When corroborating a client's SoW, the Bank must use Corroborative Evidence (“CE”) i.e., objective information to substantiate the plausibility of the client’s wealth journey and should not rely solely on the client’s representation. Even with the SOW template, RMs still find it challenging to follow the template to write consistently and coherently with the CE.
  3. Prolonged turnaround times and client friction: The traditional SoW authoring process often spans several weeks, with multiple rounds of to-and-froing between internal review and client follow-ups. Such delays inconvenient clients, particularly those already accustomed to fuss-less digital experiences.

In view of the above challenges, Bank of Singapore identified a strategic opportunity to adopt Generative AI to modernise the SoW authoring process.

This solution leverages Generative AI to enhance productivity, ensure risk-proportionate assessments, and uphold principles of Materiality, Prudence, and Relevance as outlined in the MAS circular on establishing source of wealth of customers issued on 26 July 2024.

Solution overview

The PoC presents the integration of Generative AI technology to automate the write-ups of SoW. This advancement provides a baseline, well-structured and consistent standard for RMs to build upon and complete a reasonable and risk-proportionate SoW write-up. This solution could also be positioned as a strategic tool to ensure new RMs are guided by the Bank’s compliance standards in addition to the risk training they have undergone.

The implementation is structured according to the following solution design.

Figure 1: Solution Overview

Key features include:

  1. Automated data extraction: Using advanced AI models to gather information from structured and unstructured sources.
  2. Content Generation: Producing comprehensive, regulatory-compliant SoW narratives tailored to individual clients. By utilising on-premises Large Language Models (“LLMs”), the Bank can create tailored SoW for each unique client while ensuring the utmost protection of sensitive customer information. This approach mitigates the risk of client data exposure to the cloud-based LLMs.
  3. AI Agentic-Based SoW Validation: Leveraging Agentic AI framework to generate SoW, followed by AI agent validation through Proofreader. The Proofreader validates all the SoW output generated, ensuring that it can pass fact check and key statements have proper citation.

Figure 2: Agentic AI Framework

4. Data-driven comparison: Leveraging proprietary data from OCBC Group to compare the client’s wealth accumulation against industry peers, geographic regions and professional designation.  

5. Human Validation: Ensuring accuracy and alignment with MAS guidelines through control function review.

Implementation framework

The project followed a structured approach:

  1. Key model output assessment criteria:
  • The Focus User Group reviewed the AI SoW output based on five key criteria: accuracy, completeness, relevance, explainability, and industry benchmarking. These criteria are thoughtfully designed to address emerging AI risks highlighted in the “Emerging Risks and Opportunities of Generative AI for Banks” paper prepared by the MindForge Consortium which includes banks, technology firms, consulting firms, The Association of Banks in Singapore and MAS.
  • The model underwent several rounds of refinement, with each iteration informed by evaluation results. This cyclical process led to progressive improvements in scores across key criteria, addressing earlier gaps and enhancing overall model quality. Consequently, this iterative model refinement is to ensure robust and responsible AI outcomes.
  1. Data capture and pre-processing:
  • Extracting relevant client information from documents using Visual Language Model (“VLM”) and Natural Language Processing (“NLP”).
  • Classification and annotation of documents.
  • Pre-processing of data for normalisation and meta-data update such as sensitive information identification.
  1. Classification and summarisation:
  • Categorising client profiles based on risk levels.
  • Summarising financial history and wealth sources into logical grouping of similar information types and accompanying concise narratives.
  1. Generative AI integration:
  • Leveraging LLM to draft SoW reports with limited hallucination.
  • AI feature is being integrated into the Bank’s client onboarding system workflow, where RMs/ Assistant Relationship Managers (“ARMs”) use to create new portfolios during onboarding and now has the feature to help them draft out the SoW write-up.
  1. Validation workflow:
  • RMs/ ARMs validate AI-generated outputs against regulatory requirements before final submission.

Collaboration structure

A key success factor of this initiative was the strong cross-functional collaboration among specialised teams, each contributing critical expertise to the project’s success.

Bank of Singapore’s Financial Crime Compliance Platform & Analytics Team: This team of subject-matter experts and project managers led the initiative, identifying the inefficiencies in the SoW write-up process and envisioning how Generative AI could address them. They defined the compliance requirements, outlined edge cases (e.g., complex client profiles), and ensured that the AI-generated narratives adhered to regulatory standards.

Bank of Singapore’s Operations and Technology Department: The IT Architecture and Engineering team provided the technological foundation for the PoC. They were responsible for integrating the LLM into the Bank’s infrastructure, establishing secure development and testing environments, and ensuring seamless workflow integration with existing onboarding systems.

OCBC Group Data Office: As the AI and data science centre of excellence within the OCBC Group, this team played a pivotal advisory role. Their data scientists and engineers supported model selection, fine-tuning, and performance evaluation. They also ensured the project adhered to OCBC’s AI governance framework, including alignment with MAS’s FEAT (Fairness, Ethics, Accountability, Transparency) principles.

Focus User Group: This user group consists of Financial Crime Compliance and 1.5 Line of Defence Reviewers who are the reviewers for the SoW assessment. Their responsibility is to co-create the solution ensure the acceptance baseline standard which consequently leads to user adoption.

Pilot RM User Group: This pilot group comprises RMs who are the end users of the AI solution. Pilot RM users were empowered to have the firsthand experience in writing SoWs using AI. Their early feedback of the solution has proven to be invaluable and critical for model refinement to ensure high user adoption.

Steering Committee and oversight: A governance body comprising senior leaders from Financial Crime Compliance, 1.5 Line of Defence, IT, and the Group Data Office provided strategic oversight. This committee reviewed project milestones, managed risks, and established clear go/no-go criteria for transitioning from PoC to pilot or production.

The project was executed using an agile methodology, with regular cross-functional workshops to iterate on the model’s performance and incorporate feedback from frontline compliance users.

Ultimately, the success of this initiative was driven by a shared vision and a spirit of collaboration. Bringing together the front office, 1.5 Line of Defence, Compliance, Technology, and Data Science teams, Bank of Singapore was able to deliver a transformative solution that not only met regulatory obligations but also set a new benchmark for innovation in financial compliance.

Early results and key learnings

As the PoC nears completion, Bank of Singapore has gathered a wealth of insights on the performance of the Generative AI approach and the practical considerations of deploying it in a compliance setting.

Here are some results and learnings:

  • Focus User Group: The focus user group is an instrumental user group, consisting of SoW reviewers from Financial Crime Compliance and 1.5 Line of Defence. This user group played an important role in co-creating the solution and its acceptance baseline standards which consequently led to user adoption.
  • Time Reduction for SoW Draft Creation: In trial runs, the AI system reduced SoW draft creation time by 20% to 50%.
  • Accuracy, Completeness and Hallucination: AI drafts showed promising accuracy, correctly identifying key wealth events in straightforward cases with minor tweaks needed. In complex cases, it missed subtleties. Regardless of the case complexity, hallucination was observed. This risk was mitigated by human reviewers from focus user group who caught and feedback for model training. Furthermore, the integration of an AI Agent Framework Proofreader has added an additional layer of quality assurance to mitigate the hallucination. The PoC team is cataloguing these issues to further train the model and improve prompt design. The PoC team goes a step further to brainstorm a transition page for RMs to verify the extracted data by AI to institute the “Human-in-the-loop” control in model implementation.
  • RMs/ ARMs Feedback: RMs/ ARMs were initially sceptical but became cautiously optimistic after using the AI. However, they later found it easier to edit AI drafts than writing from scratch, appreciating AI’s assistive role in ensuring baseline compliance standard. Additionally, the standardised format produced by the AI facilitates SoW write-up and review, allowing RMs/ ARMs and reviewers to have common understanding on the expectation of SoW corroboration as well as SoW write-up that adhere to Bank’s compliance standards.

Limitations discovered

  • Unstructured data extraction: The AI struggled with reading financial reports that are of different standards and formats. This hampered the accurate extraction of financial figures such as revenue, net profit, dividends and shareholder equity. Finetuning the model with the most common financial reports standard such as Financial Reporting Standards (“FRS”) and US Generally Accepted Accounting Principles (“GAAP”) is a mitigating action taken in the project. However, this action requires labour-intensive annotation.
  • Ongoing feedback: The findings underscore that AI solutions in compliance require ongoing user feedback and maintenance, not a one-time deployment, to adapt to evolving rules and practices.
  • Document processing: A major challenge stemmed from the variability in the types of PDF documents uploaded by users. These fell into two categories:
  1. Machine-readable PDFs – Containing embedded text that can be directly parsed using standard libraries.
  2. Scanned PDFs – Image-based documents that require advanced processing to extract textual content.

Initially, the system struggled with scanned PDFs, which lack extractable text. To address this, we integrated a Visual Language Model (“VLM”) capable of interpreting and extracting text from image-based documents. This significantly improved the system’s ability to handle non-machine-readable inputs. However, the VLM’s performance is still limited by the quality of the scans. poorly scanned or low-resolution documents continue to pose challenges, often resulting in partial or inaccurate extraction.

  • Model Performance Constraints: From a performance perspective, the system required substantial computational resources to support the VLM and latest updated LLM. Latest VLM and LLM require Graphics Processing Unit (“GPU”) to ensure reliable document analysis and inference. These high-performance GPUs enabled efficient handling of complex visual-textual data, though model accuracy remains sensitive to input quality. Ongoing efforts are focused on improving robustness against noisy inputs and optimising GPU utilisation to ensure scalability and responsiveness in production environments.

Journey beyond PoC

Bank of Singapore’s Generative AI PoC demonstrates the powerful combination of human expertise and artificial intelligence. It has significantly improved a process that was traditionally seen as laborious and time-consuming. By using AI to generate initial drafts of compliance narratives, the Bank has standardised the SoW write-up and consequently shorten the preparation duration by 20% to 50%.

Importantly, early results show that baseline quality draft and speed in the preparation and review process can go hand in hand. In a nutshell, with strong governance, human oversight, and well-defined controls, the AI system maintains the high standards required for regulatory compliance while improving operational efficiency.

A key driver of this progress is the feedback loop built into the AI workflow. Feedback from RMs and reviewers are captured and used to improve the model. Bank of Singapore is developing a structured approach to harness these insights, allowing the AI to be fine-tuned and retrained to better understand context and regulatory expectations. This continuous learning process will help the model become more accurate, consistent, and aligned with real-world use cases.

The Bank also sees potential for broader adoption across departments within the OCBC Group, creating opportunities to standardise and streamline compliance documentation at scale. With the continued support of MAS, this initiative reflects a responsible and forward-looking approach to innovation in financial services. It shows that compliance can be both effective and efficient when supported by the right technology and governance.

This marks the beginning of a broader transformation. Bank of Singapore envisions AI as a trusted partner in compliance—supporting professionals, improving oversight, and helping to build a more responsive and resilient financial ecosystem.

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Author:
Bank of Singapore
Related issues:
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