For years, the promise of fintech in Southeast Asia was to take paperwork out of finance. Cash became wallet balances. Invoices moved from filing cabinets into enterprise resource planning systems. Payment approvals shifted from email chains to dashboards. The broad direction was clear: digitise what used to be manual.
But a new report by Sunrate and Mastercard argues that this phase is no longer enough. In “Beyond Automation: Defining Agentic Global Payments”, the companies describe a shift from automation to autonomy, a model where AI systems do not merely assist finance teams, but carry out tasks across the payment lifecycle within set boundaries.
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The phrase used in the report is “Agentic Global Payments”. In plain terms, it refers to AI agents that can observe what is happening in a financial workflow, assess the best course of action, make decisions, and execute them. Rather than waiting for a human to approve every operational step, these systems can route payments, manage liquidity, flag exceptions, and optimise timing based on real-time conditions.
For Southeast Asian startups, particularly those expanding across borders, this could mark a significant change in how finance operations are built. The question is no longer whether payments can be digitised. It is whether they can increasingly run themselves.
From automation to autonomy
The report frames the evolution of payments in three broad phases.
The first phase was digitisation and rules-based automation. This is where much of the region’s fintech infrastructure has focused over the past decade: moving transactions online, automating batch processing, and replacing repetitive administrative work with software. A system could be told that if a condition was met, a payment should be triggered or a notification sent.
The second phase brought machine learning into financial operations. Instead of following only fixed rules, systems could identify patterns and make predictions. Fraud scoring, credit risk models, and customer segmentation tools all fit into this stage. These systems improved decision-making, but usually still required people to interpret outputs and act on them.
The third phase, according to the report, is agentic execution. This is where systems are designed to do more than recommend. They can act. An AI agent might monitor cash positions across currencies, compare payment rails, consider fees and settlement times, and execute the most suitable transaction path without waiting for a staff member to manually coordinate each step.
That distinction matters. Many companies already use automation, but most workflows still depend on human intervention when conditions change. If a supplier asks to be paid in another currency, if a payment rail becomes slow, or if foreign exchange volatility affects the timing of a transfer, someone in finance typically has to step in. Agentic systems aim to reduce that dependence.
Why manual coordination is becoming a bottleneck
The case for agentic payments is being driven by a widening gap between the speed of commerce and the capacity of finance teams.
Startups in Southeast Asia often scale across markets before they have large back-office teams. A Singapore-based company may sell into Indonesia, hire in Vietnam, source from China, bill clients in US dollars, and pay partners in multiple local currencies. Each market brings its own banking practices, tax rules, compliance expectations, payment rails, and settlement timelines.
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The result is operational complexity. Finance teams must reconcile accounts, manage cash flows, track foreign exchange exposure, approve payments, and respond to exceptions across markets. Even with modern software, much of this work still sits between systems. A treasury tool may not speak neatly to an enterprise resource planning platform. A payment gateway may not give enough visibility into liquidity. A banking portal may require separate manual checks.
This fragmentation is common in fast-growing companies. Tools are often added as needs arise, creating a patchwork of systems that solve individual problems but do not always provide a clear view of the whole financial operation.
The Sunrate and Mastercard report describes agentic AI as a move towards a “one brain” model for finance infrastructure. Instead of separate systems handing off partial information, an intelligent layer could coordinate decisions across payments, treasury, compliance, and reporting.
In practice, that could mean a system that understands not only that a payment must be made, but also how it should be made, when it should be sent, which route is cheapest, whether there is enough liquidity in the right currency, and whether any regulatory checks need to be completed first.
Why Southeast Asia is a likely testing ground
Southeast Asia is a natural market for this shift because cross-border complexity is built into the region’s startup economy. Unlike the US or China, where companies can scale across a large domestic market, Southeast Asian startups often face international operations early.
A company expanding from Singapore into Indonesia, Thailand, the Philippines, Malaysia, and Vietnam is dealing not just with new customers, but with different currencies, banking systems, consumer payment habits, and regulatory frameworks. Even within digital commerce, fragmentation remains a defining feature of the region.
This makes payment orchestration: the process of choosing and managing the best payment method, provider, currency, and route, especially important. For startups with thin margins, small differences in fees, settlement delays, or foreign exchange timing can affect working capital. For companies handling high volumes of transactions, manual decisions do not scale well.
An AI agent, in this context, could automatically determine the most efficient rail for a supplier payout, adjust timing based on currency movements, or identify a liquidity shortfall before it becomes an operational issue. It could also help finance teams focus on higher-value judgement calls instead of repetitive coordination.
The report cites research suggesting that by 2025, 85 per cent of enterprises say they will use AI agents across various use cases, with 78 per cent of those being small and medium-sized businesses. It also notes that 33 per cent of industrial B2B firms are expected to have deployed AI-powered buyer agents to make purchasing decisions.
These figures point to a broader shift: AI agents are moving from experimental tools into operational systems. Payments may be one of the areas where the impact becomes visible quickly, because the pain points are measurable — failed transactions, delayed settlements, high fees, trapped liquidity, and compliance friction.
The limits and risks of autonomy
Still, the move towards agentic payments raises questions that cannot be ignored.
Finance is not a low-stakes environment. A poorly configured AI agent could route money incorrectly, miss a compliance red flag, or optimise for cost while creating new operational risk. In Southeast Asia, where regulatory requirements vary widely by market, businesses will need strong guardrails before handing more authority to autonomous systems.
Trust will be central. Companies will want to know how decisions are made, what data is used, who is accountable when something goes wrong, and how quickly human teams can intervene. For regulated sectors such as fintech, lending, and remittances, these questions become even more important.
There is also the issue of readiness. Many startups still struggle with basic financial data hygiene. If invoices, customer records, bank feeds, and compliance data are incomplete or inconsistent, an AI agent may simply make faster decisions on flawed information. Autonomy depends on infrastructure, and not every company has that foundation in place.
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For founders, the practical lesson is not to replace finance teams overnight. It is to understand where manual coordination is creating the biggest drag, and where intelligent systems can safely take on more responsibility.
What this means for the next generation of fintech
The first wave of Southeast Asian fintech helped consumers and businesses move money digitally. The next wave may be about making financial operations less visible, not because they matter less, but because they happen with fewer manual steps.
If agentic payments mature, finance teams may spend less time checking dashboards and more time setting strategy, defining risk limits, and managing exceptions. Payment infrastructure companies, meanwhile, will compete not only on coverage or fees, but on how intelligently their systems can act across markets.
For the e27 community, the implication is clear. The next major opportunity in fintech may not be building another tool that helps humans move money more efficiently. It may be building the autonomous layer that decides how money should move in the first place.
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