Ask any digital lender in Jakarta, Manila, or Ho Chi Minh City what stops them from approving more loans, and the answer is usually the same: most applicants have no credit history to score. Southeast Asia is one of the most underbanked regions in the world. Large shares of adults in Indonesia, the Philippines, and Vietnam have never held a formal loan or credit card, so a traditional bureau check comes back empty. The borrower might be perfectly creditworthy. The lender just has no way to prove it.
AI credit scoring on alternative data exists to fill that gap. This is a practical look at what it does, who the players are, and where it actually helps.
The thin-file problem
A "thin-file" borrower is someone the credit bureau barely knows. In mature markets that is a small minority. In much of SEA it is the majority of the addressable market: gig drivers, market traders, factory workers, and small-business owners who run on cash and a phone. A bank that only lends to people with a clean bureau record is fishing in a tiny pond while the real demand sits outside it.
The old workaround was to lend cautiously and price in the risk, which means high rates and low approvals. Alternative-data scoring takes a different route. Instead of asking "what is this person's credit history," it asks "what can we infer about repayment behaviour from other signals."
What alternative-data scoring actually uses
The signals vary by provider, but the common ones are device and app-usage patterns, e-wallet and transaction behaviour, telco data, and the way someone fills in an application. None of these is a credit score on its own. The model combines them to estimate the probability of default, then hands the lender a score they can act on.
Done well, this opens lending to people who were invisible before. Done badly, it bakes in bias or scores people on signals they cannot see or contest. That tension is the whole story of this category, and it is why regulation matters as much as model accuracy.
The players
Several companies focus on this for SEA. Bizbaz, based in Singapore, builds AI scoring, fraud detection, and eKYC for banks and fintechs, and has piloted with banks in the Philippines and Indonesia; its pitch is profiling thin-file borrowers from financial, lifestyle, and digital footprints. CredoLab scores from smartphone metadata with the user's consent and is widely used by SEA lenders. Trusting Social, with deep roots in Vietnam, scores at population scale using telco and alternative data. Advance.AI and HyperVerge both pair scoring with strong eKYC and identity tooling, which matters because onboarding fraud and credit risk are two sides of the same problem in this region.
My read: if you are a bank bolting AI onto an existing loan process, the integrated players that combine scoring with fraud and eKYC save you stitching three vendors together. If you are a fintech that already has identity sorted, a focused scoring engine may be the cleaner fit.
Pricing and how it is sold
This is enterprise software, so there is no public price list. Deals are quote-based and usually priced per score or per application volume, sometimes with a platform fee on top. For a lender, the maths is straightforward: if alternative-data scoring lets you approve a meaningful slice of applicants you would otherwise reject, and they repay, the per-score cost is trivial against the interest earned. The risk is the opposite case, where loosened approvals raise defaults. That is why most lenders run these models in shadow mode first, scoring real applicants without acting on the score, to check the model against actual repayment before trusting it.
The cautions that matter
Three things are worth keeping front of mind. First, regulation is tightening. Regulators in Indonesia, the Philippines, and Singapore are paying closer attention to how alternative data is collected and whether borrowers consented. A model trained on data you cannot legally use is a liability, not an asset. Second, explainability is not optional. If a regulator or a rejected applicant asks why a loan was declined, "the model said so" is not an answer. Favour providers that can explain a score. Third, local validation is everything. A model tuned on Vietnamese telco data will not transfer cleanly to the Philippines. Insist on validation against your own portfolio in your own market.
The verdict
AI credit scoring on alternative data is one of the few AI use cases in SEA finance with a clear, measurable payoff: more approved borrowers without a matching jump in defaults. For banks and fintechs lending into Indonesia, the Philippines, and Vietnam, it is close to essential if you want to reach beyond the thin slice of customers the bureau already knows. Pick a provider that fits your stack, demand explainable scores, run it in shadow mode before you trust it, and keep your compliance team in the room from day one. The technology is ready. The discipline around it is what separates a smarter loan book from a riskier one.