The easiest AI metric to report is the number of use cases launched. It is also one of the least useful.
Asia Pacific lenders now need a tougher standard. The question is no longer what AI can demonstrate. It is what improves for the business, the customer and the credit team.
That standard matters in a region where banks pursue efficiency, SME growth, financial inclusion, digital customer expectations and stronger risk management at the same time.
Banks see AI's defensive value more clearly than its growth value
The Asian Development Bank's 2025 Global Trade Finance Gap Survey asked more than 110 trade finance providers about AI. Over 84% of bank respondents said they see potential for AI in fraud prevention and risk management. Nearly 56% said their bank is assessing whether AI could increase its trade financing capacity.
The survey covers trade finance, not lending as a whole. The pattern still matters for lenders. Most banks see potential for AI in defence. Fewer are assessing it as a source of growth, and proving where that growth comes from is the harder task.
Speed is a commercial advantage in SME lending
For many SMEs, the experience of borrowing depends on how quickly and simply finance arrives.
A study by the Asian Development Bank Institute and the Cambridge Centre for Alternative Finance, published in January 2025, surveyed 819 MSME users of digital finance platforms in Bangladesh, China, India, Kazakhstan, Mongolia, Pakistan and Viet Nam. Most had approached a bank before turning to a digital platform: 73% had sought bank funding, and only 57% of those secured it.
When choosing a digital platform, more than 70% of the MSMEs named better customer service and better approval rates as the most important factors. Speed in receiving funds and a less complex application process were also important. After financing, 82% reported higher revenue, 79% higher profit and 79% a larger customer base.
These are self-reported results from users of digital finance, not evidence that AI creates them. They do show what these SMEs value in a lender: approval, service, speed and simplicity. Most of them had approached a bank before.
If AI helps a bank organise information sooner, catch missing documents earlier, support quicker analysis or prioritise straightforward cases, it contributes to faster decisions. In a competitive market, that speed becomes part of the offer to the customer, not just an internal efficiency figure.
Better information can widen access without lowering standards
Asia Pacific has large populations of SMEs and individuals whose financial profile traditional documentation does not capture well. As digital ecosystems deepen, banks gain access to more behavioural, transactional and alternative data.
AI can make those larger data sets usable for segmentation, credit assessment and monitoring, especially where one standard data source does not explain the customer.
The aim is not to lower credit standards. It is to understand risk better and find viable customers more efficiently.
Portfolio visibility is an underrated use case
The value of AI does not stop at origination. Borrower conditions in Asia Pacific can shift fast, through supply chain disruption, commodity prices, currency moves, climate events or changes in domestic demand.
Risk teams cannot investigate every exposure with the same intensity every day. Better monitoring directs their attention to the accounts that need review.
The value is not perfect prediction. It is earlier prioritisation and better use of specialist time.
Adaptability matters as much as intelligence
Asia Pacific banking strategies change market by market. A product designed for Singapore may need a different process in Indonesia. SME lending in Viet Nam may rely on different information from a corporate facility in Australia. A regulatory change can force a bank to adjust policies, approval rules or controls quickly.
AI cannot be separated from process flexibility. AI can spot an opportunity, but the bank still needs an operating environment that can respond. If every product, workflow or rule change needs a lengthy technology project, faster insight is worth much less.
Business value needs business metrics
A stronger AI scorecard asks:
- How much analyst time was recovered, and where did it go?
- How did application turnaround change?
- Can the bank handle more cases without more operational burden?
- Are exceptions identified sooner?
- Are decisions more consistent?
- Is portfolio visibility better?
- Can the bank change products faster?
For SME and financial inclusion strategies, banks can go further. Can more viable customers be assessed economically? Can smaller-ticket lending be served sustainably? Do digital channels reach customers beyond the major cities?
Start with the lending problem, not the technology
The biggest risk in the current AI cycle is building a long list of use cases because the technology is available.
A better starting point is to find where the lending business loses time, quality, visibility, flexibility or reach. Then decide which mix of AI, automation, data and process redesign fixes it.
Sometimes AI will be central. Sometimes a simpler workflow change creates more value. Sometimes the bank needs better data before it needs a better model.
Where ACP fits
One Asia Pacific result on Axe Credit Portal (ACP) shows the kind of scorecard that matters.
A top-tier bank in Vietnam automated its credit process on ACP from application through approval, across corporate, SME, FI and NBFI lending. The result: 3.5x faster time to yes, a 40% increase in credit application volume and 80% lower operational risk. ACP integrates with 15 third-party systems at the bank and gives every credit stakeholder one entry point.
Those are scorecard measures: turnaround, capacity and control. Three more parts of the scorecard map to ACP capabilities.
- Adaptability. With ACP Studio, the bank reconfigures workflows, business rules and approval chains itself, without vendor intervention or a development cycle. A product or policy change in one market does not wait for an IT project.
- Portfolio visibility. ACP Early Warning System lets the bank configure the signals, thresholds and rules that flag deterioration. Configurable alerts feed escalation workflows that generate recommended next actions. Portfolio dashboards show KPIs and risk metrics across segments.
- Analyst time. ACP's configurable AI credit write-ups use RAG-based contextual intelligence. An interactive writing assistant lets the analyst refine the draft.
The advantage is in the application
As advanced AI becomes widely available across the region, access to the technology will differentiate less. The advantage will come from how well each bank applies it to the customers, products and markets it serves.
The objective is not to maximise AI adoption. It is to build a better lending business.
Meet us at Singapore FinTech Festival 2026
to · Singapore EXPO
Meet Joe Udomdejwatana and Rose Dmello to discuss:
- which lending problems in your bank AI should tackle first, and which need a workflow fix
- how to build an AI scorecard on lending metrics, not use-case counts
- what changed for a top-tier bank in Vietnam after automating its credit process on ACP
If the calendar does not load, open Joe's booking page.
If the calendar does not load, open Rose's booking page.
References
- Beck, S., DiCaprio, A., Pandey, A. and Malaket, A. ADB Global Trade Finance Gap Survey. ADB Briefs No. 378, Asian Development Bank, December 2025. DOI: 10.22617/BRF250557-2.
- Cambridge Centre for Alternative Finance and Asian Development Bank Institute. MSME Access to Digital Finance Study: In Selected EMDE Countries in Asia, January 2025.