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FC Insights | December 2025
AI in Lending: 3 Breakthroughs Redefining Fintech in Southeast Asia
Foreword
AI is no longer a future concept in fintech lending. It has now started to reshape how credit is assessed, managed, and scaled across markets.
In our latest analysis, we take a closer look at the growing synergy between fintech and AI, focusing specifically on the lending landscape. We highlight three core AI use cases, illustrated by examples from global players, and share our perspective on how regional companies can accelerate growth by adopting similar approaches while building their own moats.
The use of AI in lending has also been translated into tangible results:• 80-90% higher accuracy for default risk prediction• 28% reduction in non-performing assets; and• 35% improvement in on-time repayments
These impacts demonstrate AI's ability to drive stronger performance and sustainable growth for lending. As AI becomes increasingly essential to modern lending, we foresee strong momentum toward later-stage funding and broader sector expansion, despite capital currently remaining concentrated in early-stage ventures.
In our latest analysis, we take a closer look at the growing synergy between fintech and AI, focusing specifically on the lending landscape. We highlight three core AI use cases, illustrated by examples from global players, and share our perspective on how regional companies can accelerate growth by adopting similar approaches while building their own moats.
The use of AI in lending has also been translated into tangible results:• 80-90% higher accuracy for default risk prediction• 28% reduction in non-performing assets; and• 35% improvement in on-time repayments
These impacts demonstrate AI's ability to drive stronger performance and sustainable growth for lending. As AI becomes increasingly essential to modern lending, we foresee strong momentum toward later-stage funding and broader sector expansion, despite capital currently remaining concentrated in early-stage ventures.
FC Insights
Nearly 50% of adults in Southeast Asia are unbanked or underbanked according to the World Economic Forum, mainly due to structural barriers like limited credit history and low financial literacy. These constraints continue to limit access to formal credit and widen gaps in trust and understanding between consumers and financial institutions. In response, technology-enabled lending has emerged to lower entry barriers and expand access to formal financial services across underserved segments.
As access to credit expands, lenders face heightened risks from inaccurate credit assessment, which can result in higher default rates. This is where AI plays a critical role, by improving borrower assessment, detecting emerging risks earlier, and reducing potential losses to support sustainable growth in the lending industry.
As access to credit expands, lenders face heightened risks from inaccurate credit assessment, which can result in higher default rates. This is where AI plays a critical role, by improving borrower assessment, detecting emerging risks earlier, and reducing potential losses to support sustainable growth in the lending industry.
In what ways is AI transforming the lending landscape?
1) Improved eKYC and onboarding process
AI gathers customer data and makes quick assessments for eKYC and onboarding, speeding up screening and reducing manual verification, and also detects possible fraud like stolen identity, deepfakes, or credit washing.
2) A more flexible and accurate credit scoring systemAI uses diverse source of data to build ICS (Innovative Credit Scoring) and customer profiles, improving assessment accuracy for unbanked and underbanked users, and lowering default risk.
3) Automated, human-like credit collectionsAI supports collections by sending automated reminders or replacing the call agents to push repayment, complementing its KYC and risk prevention role.
How does AI significantly impact the lenders’ loan management?
One global player reports that its AI models improve default prediction accuracy by 80-90% compared to traditional methods, while other AI-driven loan platforms automate around 60-80% of credit decision-making, significantly accelerating approval timelines.
This improvement is also seen in the SEA landscape, where one mid-sized Southeast Asian bank tried to use AI for its loan management on origination, scoring, and collections. The bank reported a 28% reduction in NPAs (non-performing assets), 35% increase in on-time repayments, and 40% faster credit approvals.
What is the conviction that AI-driven lending products are in high demand from the market?
Investors are actively backing AI-driven lending companies in Southeast Asia, as evidenced by a growing number of deals and sizable funding rounds, highlighting strong market interest. One noticeable pattern is that several recently funded AI lending companies are replicating proven global solutions, such as AI-driven underwriting and fraud detection tools like Zest AI, or AI-driven lending decisioning systems like Quantum Lending Solutions.
Given this, we expect players to demonstrate model robustness and adapt their solutions to Southeast Asia’s specific market dynamics and regulatory needs.
Given this, we expect players to demonstrate model robustness and adapt their solutions to Southeast Asia’s specific market dynamics and regulatory needs.
What’s the next step to expect from the AI in the lending landscape?
As investor interest grows, momentum is building. The key challenge is whether existing players can prove their edge, capture meaningful market share, and scale from early stage to growth and eventually late stage funding. This requires a clear cutpoint to demonstrate a defensible moat.
1) Regulatory compliant within the market. Fintech remains a regulatory-first vertical, requiring players to always comply with the required regulations in each market they are operating in.
2) Proprietary datasets for information processing. Developing an AI-powered lending management system requires access to extensive datasets that go beyond basic identity and financial records, incorporating tax history, e‑commerce transactions, and other alternative data to enhance model accuracy and outcomes. Building such a comprehensive database typically necessitates partnerships with banks, government agencies, and other relevant stakeholders to effectively train the AI model.
3) Build an explainable AI model (XAI). AI-powered lending platforms need model explainability to make machine learning decisions transparent to humans and move away from “black box” systems. Explainable AI helps lenders justify credit decisions, monitor model performance, and ensure fairness, combining predictive accuracy with trust and regulatory compliance.