Ask any digital lender in Jakarta, Manila, or Ho Chi Minh City what stops them from approving more loans. The answer is almost always the same. Most applicants have no credit history to score. Southeast Asia is one of the most underbanked regions on earth. Huge shares of adults in Indonesia, the Philippines, and Vietnam have never held a formal loan or a credit card. So a traditional bureau check comes back empty. The borrower might be perfectly creditworthy. The lender just has no paper trail to prove it.
AI credit scoring on alternative data exists to fill that gap. Here is what it does, who the players are, and where it actually earns its keep.
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. Think 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. The real demand sits outside it.
The old workaround was to lend cautiously and price in the risk. That 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 other signals say about how they repay.
What alternative-data scoring actually uses
The signals vary by provider. The common ones are device and app-usage patterns, e-wallet and transaction behaviour, telco data, and the way someone fills in an application form. None of these is a credit score on its own. The model combines them to estimate the probability of default. Then it hands the lender a single number they can act on.
Done well, this opens lending to people who were invisible before. Done badly, it bakes in bias or judges people on signals they cannot see or contest. That tension is the whole story of this category. 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. It 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 SEA lenders use it widely. 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. That combination matters here, because onboarding fraud and credit risk are two sides of the same problem.
My read: if you are a bank bolting AI onto an existing loan process, the integrated players save you stitching three vendors together. If you are a fintech that already has identity sorted, a focused scoring engine is probably the cleaner fit.
Pricing and how it is sold
This is enterprise software, so there is no public price list. Deals are quote-based. Pricing is usually per score or per application volume, sometimes with a platform fee on top. Ballpark numbers help, though. A single score often lands somewhere between USD 0.05 and USD 0.30 at volume. That is roughly IDR 1,500 to 5,000 in Indonesia, or about PHP 3 to 17 in the Philippines. For a lender, the maths is simple. If alternative-data scoring lets you approve applicants you would otherwise reject, and they repay, that per-score cost is trivial against the interest you earn. The risk runs the other way, when loosened approvals push defaults up. That is why most lenders run these models in shadow mode first. They score real applicants without acting on the score. Then they 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 now look closely at 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 loan book, in your own market.
The verdict
Few AI use cases in SEA finance have a payoff this clear: 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. You want to reach past 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. 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.