Business
How AI Agents are Unlocking Asia’s Banking Growth
Asia is becoming the most consequential test market for AI agents in finance, not because banks are chasing novelty, but because they are trapped between growth ambitions and an unforgiving cost structure.
Key Takeaways
- Asia as the Primary Test Market: Asia is becoming the key test market for AI agents in finance, driven by a need to meet growth ambitions and manage high operational costs, where end-to-end operations account for 60% to 70% of a bank’s cost base.
- Shift from Automation to Operating System: Agentic AI and multiagent systems are not just another automation layer; they are software capable of executing complex, multi-step work across processes and functions, moving from “innovation” to becoming the new operating system for banking operations.
- Key Challenges for Success: The transition faces significant challenges, including the need for robust governance and control over autonomous agents, surfacing and fixing deep-seated process debt, mandatory operating model change (redefining roles and accountability), and managing substantial upfront costs before realizing savings.
Agentic AI and multiagent systems promise something qualitatively different from earlier automation waves: software that can execute complex, multi step work across tools, teams, and processes, closer to how an operations employee actually works.
If that sounds like hype, consider where the pressure sits. Banking is not primarily a product business. It is a process business. In most institutions, the bulk of spend is embedded in end to end operations: onboarding, servicing, investigations, reconciliations, and the endless exception queues that accumulate whenever a rule meets reality. When AI agents can take on chunks of those workflows, they stop being “innovation.” They become the operating system.
The Rise of Agentic AI in Asian Banking
Agentic Artificial Intelligence (AI) and multiagent systems are expected to significantly transform banking operations across Asia over the next decade. According to a report by McKinsey & Co., these technologies can execute complex, multi-step tasks while integrating seamlessly across processes, people, and technology, enabling a more connected and intelligent banking ecosystem.
This advancement in banking technology gives financial institutions the ability to optimize workflows, enhance customer service, boost productivity, and reduce operational costs. The report notes that end-to-end operations currently represent an estimated 60% to 70% of a bank’s cost base, meaning that transforming these processes could unlock substantial value across the financial services sector.
Recognising this potential, financial services companies invested approximately $35 billion globally in AI in 2023. These investments are expected to accelerate rapidly, with global AI spending in the sector projected to reach nearly $100 billion by 2027.
Growth is the accelerant, not a side benefit
In Asia, the growth story matters as much as the efficiency story. Customers expect near instant account opening, faster credit decisions, real time dispute handling, and 24 7 service. Banks can hire their way to that standard, but only briefly, and only at rising cost. AI agents offer a path to scale volumes without scaling headcount linearly.
That is why investment is rising so quickly. The spend is not just about better chat interfaces. It is about building a capability that can absorb demand spikes, shorten cycle times, and reduce the friction that turns customer intent into churn.
Where the first real gains will appear
The public narrative about agents usually starts with customer chat. The more important battleground is the messy middle: document heavy work, high variability workflows, and handoffs between functions.
This is where multiagent systems become practical. One agent gathers information, another checks policy and completeness, another drafts communications, another routes approvals, another logs actions for audit, and a human signs off at the right risk point. Done well, this moves the bank from “people pushing cases” to “systems pushing outcomes,” with humans supervising decisions instead of shepherding paperwork.
And importantly, this is not one bot per niche task. The economic promise is that agentic systems can be trained, reused, and scaled across functions when the bank designs them as shared infrastructure rather than isolated pilots.
The hard part: challenges that will define winners
Governance will be the bottleneck. Autonomy is easy to demonstrate and hard to control, and that gap is where most agent programmes in banking will stumble. Banks operate inside strict requirements around accountability, privacy, audit trails, and segregation of duties. An agent that can take action must also be constrained, monitored, and explainable, with clear limits on what it can do and a reliable record of what it did. Otherwise, the institution does not become more efficient, it simply accelerates risk.
Process debt will surface fast. Many banks still run on workarounds: manual checks, spreadsheet controls, informal approvals, and tribal knowledge sitting in inboxes or living in the heads of a few key operators. Agents do not fix that by default. They expose it. The more you automate, the more your broken process definitions, inconsistent data, and undocumented exceptions turn into operational incidents that are harder to ignore and faster to amplify.
Operating model change is not optional. Introducing agents forces uncomfortable questions that most organisations avoid until a problem lands on an executive desk. Who owns the workflow when a machine does half the work. Who is accountable for mistakes? Which actions require human approval, and which can be delegated. Without redesigned roles, escalation paths, and guardrails, banks end up with parallel systems: humans redoing the same work just to be safe, while agents generate additional noise and complexity that undermines trust.
Costs can rise before they fall. The promise is a lower operating cost base, but the path often includes meaningful upfront expenses: integration work, data remediation, stronger controls, monitoring, and staff training. Banks that treat agents as plug and play will be disappointed because they underestimate how much of banking is process, policy, and accountability rather than software. Banks that treat agents as a strategic rebuild, with investment in foundations and disciplined scaling, are the ones most likely to see the cost curve bend in their favour.
Asia’s winners will industrialize agents, not experiment with them
Within a few years, “having AI agents” will be table stakes. What will differentiate Asia’s banks is execution quality.
The leaders will pick a handful of operational domains where value is concentrated, redesign the full workflow end to end, embed controls and auditability from day one, and then scale the agent capability across adjacent functions. They will measure outcomes, not activity: cycle time, error rates, rework, customer satisfaction, and cost per case.
The laggards will do what banks have done in every technology wave: launch dozens of pilots, spread thin governance, and declare progress while the underlying process remains unchanged.
AI agents are not simply another layer of automation. In Asia, they are becoming a new labor model for banking operations. The question is not whether banks adopt them. The question is whether banks can govern them, scale them, and redesign work around them before competitors do.
