The useful question for Asia Pacific credit teams is not whether AI replaces analysts. It is what analysts do with the hours AI gives back.
The region makes that question sharper than most. Sophisticated digital banking markets sit alongside large populations of SMEs, family-owned businesses and first-time borrowers. Many of these borrowers cannot be fully understood through standard data alone.
Credit decisions combine data with context
Some borrower information is easy to structure: revenue, debt, cash flow, payment history, existing exposure, collateral values and financial ratios. AI can process and compare it far faster than a person.
Other information resists a field or a score. How credible is management? Is a dip in performance temporary? How dependent is the SME on one supplier or one customer? How would a family business cope with a succession? What does the relationship manager know that no financial statement shows?
These questions weigh most where SMEs have short formal credit histories, uneven documentation or business models built on local relationships.
Research points to a division of labour rather than a contest. A 2025 Bank for International Settlements working paper on AI and relationship lending, based on data from Italian banks, puts it this way: "With its enhanced analysis of hard, verifiable and codifiable data, AI can coexist with more traditional methods." In practice, AI strengthens the analysis of hard information. Relationship banking still supplies the soft information and context.
Speed alone does not solve the credit problem
Banks face pressure to make lending journeys faster and simpler. AI can help by cutting the effort to collect, organise and interpret information.
A fast process still has to tell a strong business with unconventional documentation from a weak business that happens to fit the standard template. That is a judgement call, and it stays with experienced people.
The value of AI is in the work it removes
An experienced credit analyst adds little by searching a long document for a figure that software finds in seconds. The same goes for copying customer data between systems, preparing standard sections of a credit paper or checking several sources for policy guidance.
AI can take on much of this preparation. It retrieves information, drafts first versions, highlights inconsistencies and helps compare cases. The business value depends on what happens next.
If the analyst spends the recovered time challenging assumptions, testing scenarios, understanding the borrower and working with relationship teams, the quality of credit work rises. If AI adds another layer of output that still needs the same manual handling, the benefit disappears.
Different segments need different levels of human involvement
Asia Pacific banks often run several lending businesses in one institution: retail, SME, commercial, corporate, mortgage, development finance and Islamic finance. Each has its own operating model.
A standard consumer product can carry a high degree of automation. A complex corporate facility needs deeper interpretation of group structures, covenants, collateral, market conditions and management quality. SME lending sits between the two, especially in markets where formal data is improving but relationship knowledge still matters.
There is no single right balance between people and AI across a bank. The balance depends on the decision, the customer, the risk and the information available.
Human oversight must be meaningful, not ceremonial
Adding a reviewer to an AI process does not create control on its own. The reviewer needs enough context to challenge the output and access to the underlying information. They need the authority to override or escalate. And the case volume must be low enough that review does not become a routine click.
Supervisors are making a related point. The Monetary Authority of Singapore and the Hong Kong Monetary Authority both name accountability in the AI frameworks they issued in October 2026.
Credit roles will change
As AI takes on more information-heavy work, the skills that set strong credit professionals apart shift too. Critical thinking, commercial understanding, scenario analysis, challenge and the ability to spot incomplete or misleading information all matter more.
Relationship managers can walk into a customer conversation with a concise view of the customer and the credit. Risk teams can spend more time on unusual cases and emerging signals. Credit committees can receive better-prepared papers.
The theme is not fewer people. It is better use of their attention.
Where ACP fits
In Axe Credit Portal (ACP), the bank decides where people sit in the credit process.
- The analyst refines the AI draft. ACP's configurable AI credit write-ups use RAG-based contextual intelligence. An interactive writing assistant lets the analyst refine the draft.
- Decisions go to people with defined authority. ACP's configurable approval chains and delegation of authority route credit decisions to approvers whose authority the bank sets.
- Rules decide what needs a person. Configurable business rules hold the bank's policy limits. Configurable approval workflows route the cases those rules flag to an analyst.
- Each segment gets its own balance. ACP runs retail, SME and corporate lending on one configurable platform. The bank configures the workflows, scoring and approval chains, so a consumer product and a corporate facility can run with different levels of human involvement.
A stronger ambition than automation
Asia Pacific is too diverse for a simple automation story. Many of its most valuable lending opportunities sit where scale and context must work together: SMEs, growing corporates, cross-border businesses and customers entering formal financial services.
AI lets banks process more information, respond faster and operate at greater scale. People supply the context, challenge and accountability that complex lending still requires. The banks that combine the two will not just automate today's credit work. They will redesign it so that technology carries the repetitive load and people spend their time on the decisions that deserve their expertise.
Meet us at Singapore FinTech Festival 2026
to · Singapore EXPO
Meet Joe Udomdejwatana and Rose Dmello to discuss:
- where AI should prepare and where people should decide in your credit process
- how to keep human review meaningful as volumes grow
- where ACP's writing assistant, business rules and approval chains sit in a credit process
If the calendar does not load, open Joe's booking page.
If the calendar does not load, open Rose's booking page.
References
- Gambacorta, L., Sabatini, F. and Schiaffi, S. Artificial intelligence and relationship lending. BIS Working Paper No. 1244, Bank for International Settlements, 19 February 2025.
- Monetary Authority of Singapore. MAS sets out supervisory expectations on responsible AI adoption by financial institutions, 7 October 2026.
- Hong Kong Monetary Authority. HKMA announces "ART" Framework to promote responsible A.I. adoption, 15 October 2026.