{"slug": "the-rise-of-the-digital-colleague-how-banks-in-singapore-are-putting-agentic-ai", "title": "The rise of the ‘digital colleague’? How banks in Singapore are putting agentic AI to work", "summary": "Banks in Singapore, including DBS, OCBC and UOB, are deploying agentic AI systems that can autonomously plan and execute complex tasks across wealth advisory, client onboarding, KYC, compliance and operations, marking a shift from generative AI. The Monetary Authority of Singapore on Jul 3 unveiled the Safeguards for Agentic Finance at Runtime (SAFR) white paper to provide governance checkpoints for these systems, as risks such as cybersecurity threats remain, highlighted by OpenAI's disclosure on Jul 22 that its autonomous agents hacked another company's systems during testing.", "body_md": "The rise of the ‘digital colleague’?\n\n# How banks in Singapore put agentic AI to work\n\nAs the Monetary Authority of Singapore (MAS) tightens its regulatory oversight on artificial intelligence in the financial sector, banks are entering a new phase of AI adoption – one where digital systems are expected not just to answer questions, but to carry out increasingly complex tasks.\n\nRather than using AI just to generate content or respond to prompts, banks such as DBS, OCBC and UOB are redesigning workflows across areas such as wealth advisory, client onboarding, know-your-customer (KYC), compliance and operations around AI “agents”.\n\nThese agents can reason, plan and execute complex, multistep tasks with limited human intervention.\n\n## Decoding the differences\n\nThis shift from generative to agentic AI marks what many see as the next frontier of banking.\n\n### Generative AI: Capabilities\n\nGenerate outputs in response to prompts. Typically requires humans to decide and execute subsequent actions\n\n### Agentic AI: Capabilities\n\nPursue specified objectives by planning intermediate steps, selecting tools, and initiating actions without continuous human direction\n\n### Generative AI: Primary function\n\nCreate original content – such as text, images, video, audio or software code – in response to a user’s prompt or request\n\n### Agentic AI: Primary function\n\nDesigned to autonomously make bounded decisions and actions, with the ability to pursue complex goals under limited supervision\n\n### Generative AI:\n\nRelies on machine learning models called deep learning models trained on large datasets\n\n### Agentic AI:\n\nCombines the flexibility and natural-language reasoning of large language models with the reliability, determinism and guardrails of traditional software engineering\n\n### Generative AI: Key use\n\n- Content creation\n\n- Data analysis\n\n- Personalisation\n\n### Agentic AI: Key use\n\n- Decision making\n\n- Problem solving\n\n- Workforce automation\n\n- Planning\n\nSource: MAS, IBM\n\nWhile banks differ in how they view these systems – from sophisticated assistants to “digital colleagues” – the technology could fundamentally reshape how work is organised, with AI agents taking on a growing share of tasks alongside employees.\n\nYet, risks abound. On Jul 22, OpenAI disclosed that its autonomous agents “went rogue” and independently hacked into another company’s systems during testing, underscoring the cybersecurity challenges that come with the technology.\n\n*The Business Times* examines how banks are moving from generative to agentic AI, where these\ntechnologies are being deployed, and how institutions are aligning with MAS’ evolving AI governance\nframeworks.\n\n## Banks and their AI plans\n\nThe interactive overview covers Citi, DBS, Maybank, OCBC, Standard Chartered and UOB.\n\n### Interactive bank card carousel\n\nDrag or flick · Tap any visible card · Arrow keys rotate · Enter opens\n\n## Jobs, jobs, jobs\n\nThe shift is also reigniting concerns over the future of banking jobs. While fears of job losses have accompanied the rise of agentic AI, banks generally frame the technology as augmenting, rather than replacing, jobs.\n\nDBS chief executive Tan Su Shan has said AI will eliminate some aspects of jobs but allow employees to take on higher-value work, describing the bank's philosophy as protecting \"workers, not jobs\".\n\nOCBC similarly expects roles to “naturally evolve and change” as new technologies reshape the workplace.\n\nBut workforce transformation is just part of the story. As banks give increasingly autonomous systems a bigger role in workflows, the challenge lies in ensuring they operate safely and transparently, so they become trusted digital colleagues.\n\n## AI governance\n\nAgainst this backdrop, MAS on Jul 3 unveiled the Safeguards for Agentic Finance at Runtime (SAFR), an industry white paper providing a set of governance checkpoints that verify and record an AI agent’s proposed actions before it executes its tasks.\n\n### Agent identity\n\nIs the agent a recognised, registered agent?\n\n### Controls repository\n\nWhich controls should the proposed action be checked against?\n\n### Disposition engine\n\nHow should this action be handled?\n\n### Audit log\n\nCaptures the record, decision basis, and outcome for every action regardless of result.\n\nSource: MAS\n\nObservers have described this as a \"genuine turning point\" in moving the financial industry from broad principles to operational safeguards.\n\n\"AI agents are no longer theoretical for financial institutions,” said Bryan Keasberry, Apac head of market development at compliance software provider Fenergo.\n\n“Firms are already exploring how they can support client onboarding, compliance reviews, advisory workflows and operations, and the focus has shifted to how that can be done safely once AI starts interacting with live systems, data and decision-making processes.”\n\nChris Robinson, group chief technology Officer at IQ-EQ, agreed, saying that the conversation has evolved from whether institutions should use AI to how to deploy increasingly autonomous AI systems “safely, transparently and at scale”.\n\n## Timeline of some key initiatives on AI by MAS:\n\n-\n### MAS consultation on AI risk management closed\n\nOversight of AI risk management, policies and procedures, key AI life cycle controls\n\n-\n### MindForge AI Risk Management Toolkit launch\n\nDeveloped collaboratively with 24 leading financial institutions to manage AI risks\n\n-\n### Generative AI Guardrails in Banking Handbook published by MAS and ABS\n\nFramework for implementing Gen AI safely across financial institutions\n\n-\n### Harness AI in the Fight Against Financial Crime, in collaboration with the banking industry\n\nUse AI and machine learning to enhance scam detection capabilities\n\n-\n### Future of Finance Institute (FFI) announced\n\nNational innovation centre driving the financial sector's transition from AI and tokenisation\n\n-\n### SAFR Framework (\n\n[BuildFin.ai](http://BuildFin.ai)) white paper publishedFramework for the governance of AI agents in financial services\n\nSource: MAS\n\n## Challenges with agentic AI\n\nRobinson said the industry needs to distinguish more clearly between different categories of AI.\n\n“The risks associated with an AI tool that extracts information from documents are very different from those of an AI agent providing recommendations or supporting client-facing decisions,” he noted.\n\nKeasberry meanwhile highlights three main challenges companies face in implementing agentic AI:\n\n### Underlying infrastructure\n\nMany institutions still operate with fragmented customer data, legacy systems and manual processes spread across teams and jurisdictions. This complexity could be exacerbated when AI agents are introduced.\n\n### Governance\n\nAI adoption cannot sit with technology teams alone. Risk, compliance, operations and business functions all need to agree on where AI can be used, what level of human review is required at each decision point, and how outcomes are monitored over time. Getting that alignment across an institution is harder than building the technical capability itself.\n\n### Regulatory engagement\n\nSupervisors across the region are at different stages of readiness to accept agentic decision-making in regulated activities. Institutions will need to demonstrate not only that controls like SAFR are in place, but that those controls are effective, explainable and subject to genuine human accountability. That requires early, open dialogue with regulators rather than a compliance-filing approach.\n\nBeyond these challenges, Keasberry noted that many banks are still running AI pilots in controlled environments. The bigger challenge, he said, lies in embedding AI into regulated workflows where outputs affect customer risk ratings, due diligence outcomes or advisory recommendations.\n\nRobinson agreed, noting that the winners in the next phase of AI would not necessarily be those deploying it the fastest, but those demonstrating strong AI governance, clear accountability and robust controls alongside innovation.\n\nWhether banks ultimately view AI agents as sophisticated assistants or digital colleagues, their value will depend on earning the trust of both clients and regulators.", "url": "https://wpnews.pro/news/the-rise-of-the-digital-colleague-how-banks-in-singapore-are-putting-agentic-ai", "canonical_source": "https://www.businesstimes.com.sg/companies-markets/rise-digital-colleague-how-banks-singapore-are-putting-agentic-ai-work", "published_at": "2026-07-28 05:00:00+00:00", "updated_at": "2026-07-28 05:23:46.121952+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "ai-policy", "ai-safety"], "entities": ["Monetary Authority of Singapore", "DBS", "OCBC", "UOB", "Tan Su Shan", "OpenAI", "SAFR", "IBM"], "alternates": {"html": "https://wpnews.pro/news/the-rise-of-the-digital-colleague-how-banks-in-singapore-are-putting-agentic-ai", "markdown": "https://wpnews.pro/news/the-rise-of-the-digital-colleague-how-banks-in-singapore-are-putting-agentic-ai.md", "text": "https://wpnews.pro/news/the-rise-of-the-digital-colleague-how-banks-in-singapore-are-putting-agentic-ai.txt", "jsonld": "https://wpnews.pro/news/the-rise-of-the-digital-colleague-how-banks-in-singapore-are-putting-agentic-ai.jsonld"}}