Banking the Next 30 Million: Why Device Intelligence Is Key to Financial Inclusion in Indonesia

To reach the next 30 million borrowers, Indonesia needs scoring that reflects how people live – not just how they bank. While financial services have expanded, legacy credit systems still rely heavily on formal financial history, excluding vast populations with informal incomes, inconsistent documentation, or no credit footprint at all.

With the enactment of POJK 29/2024 legislation and the growing maturity of device intelligence, Indonesia has the regulatory and technological infrastructure to change this trajectory. Now is the time to shift from traditional scoring logic to models built on real-life behavioral signals – because Indonesia can’t close its financial gap without scoring models that see beyond bank history.

Revealing the Inclusion Gap

Despite a decade of strong progress, financial inclusion in Indonesia remains uneven. Indonesia’s adult population is approximately 205–210 million (based on a total population of ~280 million). According to the 2024 SNLIK survey, national inclusion now stands at 80.51%, but this average masks structural gaps that traditional credit systems continue to reinforce. That means roughly 19.5% (or about 40 million adults) are still excluded from formal financial services. Of these, a significant portion – about 30 million – are estimated to be credit-excluded or under-assessed.

The divide is especially stark for individuals without formal education, those living in rural areas, and youth or elderly populations.

• Many are informal workers, gig economy participants, rural micro-entrepreneurs, women without formal employment, or youth without credit history.

• These individuals may have access to basic financial services (e.g., e-wallets or savings accounts) but lack access to formal credit due to thin or no credit files.

A Turning Point: POJK 29/2024 Green Lights Data-Driven Inclusion

In December 2024, the Financial Services Authority (OJK) enacted POJK 29/2024, establishing the formal framework for Alternative Credit Scorers (ACS) – licensed providers who may use non-traditional data sources such as telco, utilities, e-commerce, and device metadata to assess borrower risk. Importantly, POJK 29/2024 requires such scoring models to be explainable, privacy-compliant, and consent‑based, and prohibits the use of traditional credit or financing data.

For Indonesia, this change is more than a regulatory update – it’s a catalyst for growth. By officially permitting the use of alternative data and enabling millions of credit-excluded individuals to access formal financing, POJK 29/2024 can stimulate consumer spending, expand small-business activity, improve quality of life, and strengthen the national tax base. The resulting increase in economic participation could have a multiplier effect across sectors, from retail to infrastructure.

This move also aligns Indonesia with global leaders in inclusive finance. The Alliance for Financial Inclusion (AFI) has stressed that closing financial gaps requires inclusive open finance – frameworks that allow customer-permissioned sharing of diverse data sources in a transparent, secure, and interoperable way.

AFI’s 2025 Policy Development and Implementation Guide for Inclusive Open Finance highlights use cases such as alternative credit scoring for microloans, digital identity verification for onboarding, cross-sector data integration (e.g., telecom, utilities), and real-time fraud detection. Device intelligence fits directly into this vision, providing consent-based behavioral and technical signals that help segment risk and responsibly expand access for underserved populations.

Device Intelligence as a New Lens on Risk

Device intelligence is the analysis of technical and behavioral signals generated when a person uses a smartphone, tablet, or computer. These signals span multiple layers:

• Technical attributes – device type, operating system, browser version, and security settings

• Behavioral patterns – navigation speed, session activity, and interaction style

• Contextual signals – time of use, geolocation, and network type

• Risk indicators – presence of spoofing tools, randomizers, or virtual machines at the browser or device level

Taken together, these signals give lenders a dynamic picture of reliability and intent. Even when an applicant has no formal credit history, device intelligence can help distinguish genuine users from risky profiles – enabling broader access to credit without weakening risk controls.

Case Study: Amartha – Lending to Rural Women with Alternative Scoring

Amartha is an Indonesian peer-to-peer lending platform dedicated to serving ultra-microentrepreneurs, with a particular focus on rural women. Instead of relying on traditional credit histories, Amartha evaluates risk using social reputation scores, group-lending models, and insights from field surveys. Offline behavioral indicators such as attendance at community meetings are combined with mobile phone usage data to create a more complete picture of borrower reliability.

Impact highlights:

• Over 1.5 million women-led MSMEs funded since inception

• Average loan size of around Rp 3 million (USD ~200)

• Repayment rates consistently above 95%

• Recognition by UNESCAP and AFI for innovative approaches to inclusion

Amartha’s case demonstrates that non-bureau-based scoring, when supported by mobile data and community trust, can sustainably extend credit to rural, credit-invisible populations.

Case Study: Jenius – Reaching Millennials with App-Based Scoring

Jenius, the digital banking platform launched by Bank SMBC (formerly BTPN), was designed to meet the needs of Indonesia’s millennials and Gen Z. Rather than assessing applicants solely on bureau data, Jenius analyzes behavioral patterns within its app – including budgeting habits, savings activity, and transaction categories – to personalize offers and extend pre-approved credit limits. This approach allows the bank to onboard and serve customers with no prior credit footprint.

Impact highlights:

• Over 3.5 million users onboarded by 2023

• High adoption among urban millennials and digital natives

• First-time credit access enabled for customers without banking history

Jenius shows how device-based behavioral signals can be leveraged for safe, low-risk experimentation with credit products, particularly for younger segments of the population.

Strategic Recommendations

To make the most of POJK 29/2024 and responsibly scale financial inclusion, institutions must treat device intelligence not just as a compliance requirement, but as a core strategic capability. The potential goes beyond meeting regulatory expectations – it’s also a path to expanding customer reach, growing portfolios, and entering untapped markets.

The biggest gains will come for microfinance institutions (MFIs), BNPL providers, digital banks, and embedded finance players. These segments often serve customers with limited or no formal credit history and can directly benefit from the richer risk insights that device intelligence provides.

Here’s how to act:

1. Deploy device-intelligence models where bureau-based scoring is ineffective or impossible – such as among the 51% of Indonesians without formal education who remain under-assessed by legacy systems.
2. Comply confidently – POJK 29/2024 supports alternative scoring systems, provided they are auditable, explainable, and consent-based.
3. Monitor impact through inclusion KPIs – focusing on rural, elderly, low-education, and gig-economy segments to measure both social and commercial outcomes.

These steps are not simply operational checkboxes. They form the foundation for building trust, improving portfolio quality, and unlocking long-term growth while responsibly broadening access to credit.

Conclusion

Indonesia has made clear strides toward financial inclusion, but the gaps highlighted in SNLIK 2024 show that traditional credit models still leave too many people behind. With POJK 29/2024 offering regulatory certainty – and both global institutions like AFI and academic research validating the effectiveness of alternative data – the case for device intelligence is no longer theoretical. It is now a critical, proven tool.

When used transparently and responsibly, device intelligence can help reach millions of underbanked individuals and strengthen the resilience of Indonesia's financial ecosystem.

Device data isn’t a workaround. It’s a strategic solution for inclusive, future-ready finance.

What Comes Next: From Insight to Action

The opportunity is clear – millions of creditworthy Indonesians are still invisible to traditional scoring models. With POJK 29/2024 in place and device intelligence ready for deployment, the tools to close that gap are already here.

The next step is execution. Whether you’re an MFI, BNPL provider, digital bank, or embedded finance player, the path forward starts with testing device-intelligence models, measuring inclusion impact, and scaling responsibly.

Book a consultation with JuicyScore to explore how device intelligence can help you grow your portfolio while expanding financial access.