Learning point 1
What is agentic AI and how is it different from what most banks have deployed so far?
Agentic AI refers to AI systems that can take autonomous sequences of actions to achieve a goal - planning, executing, and adapting without requiring human input at each step. Most banks have deployed non-agentic AI: predictive models (credit scorecards, propensity models) that take inputs and produce a score, or chatbots that answer questions. Agentic AI goes further: it acts, not just advises.
The distinction matters because it changes the value proposition dramatically. A predictive credit model tells your analyst this applicant has a 72% probability of default. An agentic credit system analyzes the application, pulls the bureau report, reads the bank statement, checks policy eligibility, calculates the recommended limit, writes the credit note, and submits it to the sanctioning authority - with the analyst only reviewing and approving the final recommendation. The agent does the work; the model only provides a score.
Agentic AI also introduces new risks that predictive models do not - specifically, the risk of the agent taking wrong actions, not just making wrong predictions. This is why evaluation, guardrails, and HITL design are so much more important for agentic systems than for traditional ML models, and why the regulatory and risk governance framework must be more robust.
In ASEAN, regulatory tolerance for agentic AI varies sharply - MAS-supervised Singapore institutions can rely on FEAT/Veritas-aligned governance to support more autonomous deployments, while Indonesian, Vietnamese, and Filipino institutions face tighter human oversight expectations driven by consumer protection emphasis.
Learning point 2
How should an NBFC think about building an internal AI capability versus buying from a vendor?
The build-versus-buy decision for AI agents in lending hinges on four factors: time to value, total cost of ownership, domain expertise depth, and strategic differentiation. For most lenders, the honest answer is: buy the platform and domain-specific agent architecture, but invest in building internal AI literacy and customization capability.
Building AI agents from scratch requires a team with expertise in LLM engineering, prompt design, agent architecture, evaluation methodology, integration engineering, and MLOps. This team costs $1M-$2.5M USD/year. This is lower in absolute terms than US/EU, but AI talent depth is meaningfully scarcer; Singapore is the only deep talent market in the region. For a lender whose core competence is credit and collections - not AI engineering - this is a significant and slow-to-produce investment.
Buying from a specialized vertical AI vendor like LendingIQ gives you agents that have already been designed for lending workflows, calibrated against multi-country bureau data including Credit Bureau Singapore, Pefindo, CTOS, NCB Thailand, CIC Vietnam/Philippines, tested against MAS FEAT alignment and country-specific data residency requirements. You go to production in weeks instead of years, and the vendor's continuous improvement benefits your deployment automatically.
The strategic case for some internal capability remains: your institution's credit philosophy, risk appetite, and customer segment create nuances that no vendor can fully anticipate. The optimal model is a vendor-provided foundation with institutional customization layers - you configure the agent's credit policy parameters, escalation thresholds, and product-specific rules, while the vendor maintains the underlying engineering.
Learning point 3
What will AI agents be able to do in banking five years from now that they cannot do today?
Over the next five years, AI agents in lending will evolve across three dimensions: capability depth, integration breadth, and trust.
On capability depth, agents will develop reliable long-term reasoning - the ability to analyze a borrower's complete credit history across multiple institutions, identify multi-year patterns, and produce credit assessments that consider the economic cycle context of individual decisions. Current agents are limited to the data in a single session; future agents will operate across longitudinal data architectures.
On integration breadth, Singapore's SGFinDex and Indonesia's PADG will mature, with Malaysia, Thailand, and the Philippines progressing parallel open banking frameworks - enabling consented, cross-account credit assessment across the region, though cross-border data flows remain restricted by national law.
On trust, MAS-supervised institutions will lead in autonomous AI adoption given Singapore's FEAT/Veritas maturity; OJK, BNM, BOT, SBV, and BSP will likely converge on Singapore-style principles over the next 3-5 years, but at uneven pace - cross-border lenders will continue to operate to the most restrictive applicable regime.
Fluent multi-language AI across Bahasa Indonesia, Bahasa Malaysia, Thai, Vietnamese, Tagalog, and Mandarin will expand credit access to the roughly $300B MSME financing gap, with particular impact in tier-2/tier-3 cities and rural regions where English-language financial infrastructure is a barrier.
For SEA, the most transformative near-term development is AI-enabled thin-file MSME lending - combining alternative data such as e-commerce transactions, ride-share earnings, telco data, and social commerce footprint with vernacular language document processing to assess creditworthiness for the roughly 50M MSMEs in the region currently underserved by formal credit.