Alternate Data Credit Scoring for the Underbanked in Indonesia

Alternate data credit scoring lets Indonesian lenders assess underbanked and thin-file borrowers that bureau data alone cannot serve. By combining Sistem Layanan Informasi Keuangan (SLIK OJK) records with compliant non-bureau signals — bank and e-wallet cash flows, utility payments, and mobile behaviour — lenders can fairly score informal-sector workers and first-time borrowers instead of declining them by default.

Who Are the Underbanked in Indonesia?

Indonesia's financial-inclusion gap is large. The World Bank's Global Findex 2021 found that around 52% of adults in Indonesia owned an account at a bank or regulated provider — meaning nearly half did not. The national picture has improved: the 2024 National Survey of Financial Literacy and Inclusion (SNLIK) by Otoritas Jasa Keuangan (OJK) and Statistics Indonesia (BPS) put the financial-inclusion index at 75.02% and financial literacy at 65.43%. Even so, roughly one in four adults remains outside the formal system, and inclusion is lower in rural areas (70.13%) than in cities (78.41%).

Many of these people are financially responsible — running warung businesses, paying rent and electricity on time, receiving regular gig or informal income — yet carry little or no footprint at SLIK OJK, the credit information system that replaced the older Bank Indonesia "BI Checking". A sparse SLIK report makes them look unscoreable.

This creates the "thin-file" borrower — creditworthy in practice but invisible to conventional scoring. Faced with an empty bureau report, a lender's instinct is to decline. But declining an unscoreable applicant is not declining a risky one; it is declining an unknown one. Within that unknown pool sits a large share of reliable borrowers who simply lack a formal record — and a real market a lender leaves to competitors.

What Is Alternate Data Credit Scoring?

Alternate data credit scoring uses financial signals that exist outside formal bureau reporting to assess creditworthiness. In an Indonesian context, the most relevant and accessible signals include:

Bank and e-wallet cash flows. Income regularity, surplus or deficit, and consistent servicing of obligations — drawn from rekening koran and digital wallet histories, the richest alternate signal in a mobile-first economy.

Utility and telecom payments. On-time payment of electricity (PLN), water, and mobile bills is a steady proxy for meeting recurring obligations.

E-commerce and platform activity. For online sellers and gig workers, sales consistency and platform ratings evidence real, recurring income.

Employer or income tenure. Salary or receipts credited from the same source over a long period signal lower income-volatility risk.

None of these is used in isolation. Their value is in how they combine — with each other and with SLIK data — to build a fuller borrower view. For deeper background, see alternate data sources for credit scoring and this alternate data credit scoring guide.

How Does Alternate Data Combine With SLIK OJK Data?

Alternate data does not replace the bureau — it complements it and fills the gap where the bureau is thin. A well-structured Indonesian model works in layers.

Where a borrower has SLIK history — or a score from a private credit bureau (Lembaga Pengelola Informasi Perkreditan, or LPIP) such as Pefindo Biro Kredit or CLIK — it stays the primary input. Where it is thin or absent, the remaining layers carry more weight and still produce meaningful risk differentiation, where a bureau-only model would return nothing at all. This is how a lender distinguishes a borrower who is thin-file because they are financially fragile from one who is thin-file simply because they have never borrowed formally. See how AI scores thin-file borrowers with no credit history.

How AI Helps

YuALT is a no-code machine-learning platform that lets Indonesian risk teams build, test, and deploy alternate-data credit models without a dedicated engineering team. It ingests bank-statement and e-wallet features, utility and telecom signals, and platform income alongside SLIK data, engineers them into model-ready inputs, and produces an explainable score — with fairness testing so signals do not become proxies for gender, region, or other protected traits. Risk teams iterate models in days, not quarters, and every decision carries an audit trail for internal governance and OJK review. The same alternate-data approach has supported over 10 million credit journeys across the YuVerse platform. The result: lenders responsibly extend credit to underbanked Indonesians who are creditworthy but invisible to bureau-only scoring.

Does Alternate Data Scoring Fit Indonesian Regulation?

Any alternate-data model must rest on clear consent and conduct principles. Under the Personal Data Protection Law (UU No. 27 of 2022), borrowers must give explicit, informed consent to the data used, personal financial data is treated as sensitive, and only signals that are necessary and proportionate should be collected. Decisions must also be explainable to declined applicants and to compliance functions.

Sound affordability assessment still applies on top of the score, and OJK consumer-protection rules (POJK 6/2022) require fair treatment and transparency. Fairness monitoring matters too: a model that leans on region or device data could disadvantage rural or lower-income applicants, so developers must test for and mitigate such effects. For the underserved syariah segment — where the 2024 SNLIK put syariah inclusion at just 12.88% — the same approach can widen responsible access.

This is a general explainer, not legal or compliance advice.

FAQ

What does "underbanked" mean in Indonesia? It describes adults with little or no formal credit footprint at SLIK OJK — often informal-sector workers, warung owners, gig earners, and first-time borrowers. They may be financially responsible but are hard to score with bureau data alone.

Does alternate data replace the SLIK OJK report? No. It complements SLIK. Where formal history exists, it remains the primary input. Alternate data fills the assessment gap for thin-file borrowers and adds depth for all applicants.

Which alternate signals work best in Indonesia? Bank and e-wallet cash flows, utility and telecom payment consistency, and platform income for online sellers are the most relevant and accessible. They are combined with SLIK data rather than used alone.

Is alternate data credit scoring compliant with Indonesian rules? It must be built on explicit consent, data minimisation, explainability, and fairness monitoring under UU PDP 27/2022, and operate within OJK consumer-protection expectations. Implementations should be reviewed by qualified legal and compliance professionals.

How does this avoid unfair bias? Through fairness testing that checks whether signals act as proxies for protected characteristics such as gender or region. A responsible model flags and mitigates these effects before deployment and keeps an explainable audit trail.

Which Indonesian lenders benefit most? Digital banks, fintech lenders (LPBBTI), multifinance companies, and syariah providers targeting the informal and first-time-borrower segments. Any lender with a high share of thin-file applications faces a choice between systematic exclusion and responsible alternate assessment.

Conclusion

In a market where roughly a quarter of adults still sit outside the formal financial system, bureau-only scoring leaves large numbers of creditworthy Indonesians unscored. Alternate data credit scoring closes that gap — combining SLIK OJK records with cash-flow, e-wallet, utility, and platform signals to assess the underbanked fairly, within UU PDP and OJK rules. Explore how YuVerse scores thin-file borrowers responsibly with YuALT.

Score the underbanked responsibly. Talk to the YuVerse team

References

• World Bank, Global Findex Database 2021 — Account Ownership — https://www.worldbank.org/en/publication/globalfindex/brief/the-global-findex-database-2021-chapter-1-ownership-of-accounts
• OJK and BPS, National Survey of Financial Literacy and Inclusion (SNLIK) 2024 — https://ojk.go.id/id/berita-dan-kegiatan/siaran-pers/Pages/OJK-dan-BPS-Umumkan-Hasil-Survei-Nasional-Literasi-dan-Inklusi-Keuangan-Tahun-2024.aspx
• Otoritas Jasa Keuangan (OJK), POJK 6/POJK.07/2022 on Consumer Protection — https://ojk.go.id/id/regulasi/Pages/Perlindungan-Konsumen-dan-Masyarakat-di-Sektor-Jasa-Keuangan.aspx
• Undang-Undang No. 27 of 2022 on Personal Data Protection (UU PDP) — https://peraturan.bpk.go.id/Details/229798/uu-no-27-tahun-2022