From Manual Triage to Autonomous Oversight: Why Agentic AI Belongs on the APAC RegTech Agenda

By Rohith Rajamony, Senior Business Solutions Consultant APAC, Smartstream

In almost every conversation I’ve had with compliance and operations leaders across APAC this year, the same tension keeps surfacing. Regulatory expectations are rising faster than team headcount, cross-border volumes keep climbing, and the back office is still absorbing most of that pressure through manual effort. Analysts in Singapore, Sydney and Tokyo are spending their days doing much the same work: chasing data across systems, applying reason codes, drafting follow-up emails, and waiting on counterparties to respond. It’s rigorous, it’s necessary, and it’s also where the bulk of operational risk quietly accumulates.

That’s the backdrop I’m bringing with me to the Global RegTech Summit APAC at Marina Bay Sands on 29 April, where I’ll be on the Demo Stage, under the Innovation programme, showcasing Smartstream’s Smart Agents solution. Ahead of the event, I wanted to share some of what’s shaped our thinking on agentic AI in this region, and why I believe it is the most consequential shift RegTech has seen in years.

When you look at what actually consumes an operations team in APAC, the pattern is striking. Industry data points to as much as 70% of back-office effort being absorbed by exception-heavy workflows: reconciliations, cash breaks, settlement exceptions, AML and KYC investigations. In APAC, those numbers carry some regional texture. Institutions here operate across fragmented regulatory regimes (MAS in Singapore, HKMA in Hong Kong, ASIC in Australia, JFSA in Japan, and more), each with its own reporting rhythms and evidentiary expectations. Add the complexity of cross-border flows, multi-currency settlement windows that rarely overlap, and counterparties on widely differing automation maturity levels, and the triage workload grows accordingly.

Most firms have invested heavily in rules-based automation and straight-through processing over the past decade. Those investments have not been wasted, but they have hit a ceiling. The work that remains – by definition the exceptions – is the work automation struggled with most: ambiguous, contextual, and dependent on tacit knowledge that lives in senior analysts’ heads.

Three things keep compliance and operations teams stuck. Data sits fragmented across internal systems, market data feeds, third-party registries, emails and messaging platforms, so analysts spend their time chasing information rather than acting on it. Institutional knowledge lives with individual SMEs rather than in the workflow itself, which means every resolution is shaped by who happens to pick up the case. And the workflow itself does not learn. The same break type arrives again next week, and the same hours are spent triaging it.

None of this is a failure of effort. It’s a structural limit of how traditional automation was designed: deterministic, rigid, and reactive. When you stack regulatory change, evolving fraud typologies and cross-border settlement onto that foundation, something has to give.

Agentic AI, and Smart Agents specifically, is a different kind of intelligence layer. Rather than responding to isolated prompts or following a fixed rule tree, an agent works toward a defined outcome (“resolve this settlement break”, “progress this KYC alert to a decision-ready state”). It plans the sequence of actions, retrieves and validates the data it needs across systems, decides on the appropriate resolution path, executes the updates, handles counterparty communication, and records everything with full auditability.

Where the traditional model has a human walking through five or six applications and an email thread, the agentic model has the data served directly to the user, with most of the journey already completed. The human judgment moment is preserved; it just happens at the point where judgment is actually required, not after a long stretch of setup work.

The numbers are meaningful. In our controlled measurements, an exception that took around 14 minutes of manual effort drops to roughly half a minute when handled autonomously. That’s close to a 29x productivity gain per break, with 30 to 60% faster resolution times and 20 to 40% fewer escalations. Honestly though, the more important shift is qualitative. Analysts stop doing swivel-chair work and start doing the oversight, risk, and pattern-recognition work they were hired for.

This is where the APAC RegTech audience should lean in. Autonomy without control is a non-starter for regulators in this region, and rightly so. MAS, HKMA and ASIC have all been clear that explainability, traceability and human accountability remain non-negotiable as firms adopt AI. An agentic system only earns its place in the back office if it can satisfy those expectations by default.

That’s why the design choices behind Smart Agents matter as much as the efficiency gains. Every action an agent takes is logged with full explainability. Governance is embedded through maker-checker workflows, policy-controlled autonomy, and human-in-the-loop escalation where judgment is required. Controls are not an afterthought bolted on to AI; they are how the system works. For compliance leaders, this turns agentic AI from a risk conversation into a control conversation, which is a very different posture.

There’s also a scaling argument that lands particularly hard in APAC. Volume spikes here – whether driven by market stress, regulatory reporting windows, or counterparty onboarding pushes – tend to arrive faster than firms can onboard headcount. An agentic workforce absorbs those spikes without the hiring, training and rota cycles that FTE scaling demands. That’s resilience, not just efficiency.

Smart Agents is purpose-built for exception-heavy financial operations, with native integration into our reconciliations, data and fees platforms, and open protocols to connect to third-party and internal systems. It brings deep domain knowledge accumulated over decades of reconciliation and operational work, paired with the agentic reasoning, orchestration and continuous-learning capabilities that modern institutions need. The emphasis throughout has been on autonomy that is accurate, compliant, and auditable, rather than clever for its own sake.

If you’re at the Global RegTech Summit APAC on 29 April, come and find me at the Demo Stage, where I’ll be walking through how Smart Agents handles real investigation workflows end-to-end, and what that looks like for a compliance team on the ground in Singapore, Hong Kong, Sydney or Tokyo. Whether or not you’re at the summit, if you’re weighing where agentic AI should sit on your 2026 roadmap, get in touch with the Smartstream team to start the conversation about what Smart Agents could mean for your operations.