Unlocking financial inclusion in the Asia-Pacific

Financial inclusion in the Asia-Pacific is at a pivotal moment. By 2030, the region is expected to comprise two-thirds of the global middle class. However, economic momentum alone does not guarantee that these communities will prosper. Access to banking, credit, lending, and insurance protections remains disparate, and many intermediaries struggle to turn scattered or inconsistent data into actionable insight.

To address these challenges, data.org, with support from the Mastercard Center for Inclusive Growth, developed Finverse, a decision-support tool and resource hub designed to help financial service providers and small- to medium-sized social impact organizations use data to strengthen financial health and resilience in their communities. The tool helps organizations navigate common obstacles, including limited data availability, paper-based collection, inconsistent reporting, and gaps in data governance and artificial intelligence use.

“We’re finally at a moment where we can build tools that don’t just offer solutions, but help communities thrive by grounding those solutions in real data,” said Cormekki Whitley, chief operating officer at data.org.

Devex spoke with Whitley about the most common data challenges that financial service providers and social impact organizations face in the region, how the tool will support sustainable growth in practice, and the role of people-centered data.

This conversation has been edited for length and clarity.

You’ve described Finverse as a “catalyst” for smarter decision-making. Could you unpack that?

We call it a catalyst because we’re not just building a tool, but rather we’re focusing on how this will actually impact people’s lives.

So many fintechs, banks, and financial institutions want to better support individuals and micro, small, and even, I’m hearing the term now, ‘nano” businesses. But to do that, they need to understand what data is available to meet the communities’ needs, confirm that the data is reliable, and identify real use cases of what is working for others. Finverse helps surface those opportunities and shows where support can make a difference for communities.

The tool focuses on five APAC countries, including Singapore, the Philippines, Indonesia, Malaysia, and India. More than helping people enter the financial system, it’s also about how they grow, sustain, and build resilience. Finverse is really a decision-support system built with people and communities, not just imposing a global north perspective. We’re working directly with individuals, community groups, and social impact organizations. And to me, that combination of people-centered design, real on-the-ground information, and a focus on community is what makes the tool catalytic.

Asia-Pacific is home to both economic growth and persistent inequality. What makes this moment particularly ripe for a tool like Finverse, and how do you see data playing a role in shaping more inclusive financial systems?

In the next decade, nearly a billion people are expected to enter the middle class in the Asia-Pacific. That creates a huge opportunity to understand what data is needed not just to help people move up, but to stay there and remain resilient through shocks such as climate events or economic downturns.

The region also offers a wide cross-section of economies. Each faces different data and AI challenges, from gaps in basic data collection and difficulty integrating systems to uncertainty around responsible AI use.

Finverse helps by surfacing real use cases already happening on the ground and showing where these data gaps are most acute. It helps organizations see what’s working, what isn’t, and what kind of information and approach they need to make better decisions. At the end of the day, data reflects people’s lived experience, and Finverse is designed to bring that into focus so providers can support financial health and resilience more intentionally.

What are some of the most common data challenges that financial service providers and social impact organizations face in the region, and how does Finverse help them overcome these?

Even now that we have an accessible, enabling technology in AI, that doesn't decrease the data challenges. Across the region, we consistently heard that there were 16 key issues. The three most common challenges that stand out are:

Limited data availability and access: Public datasets are scattered, and financial data is often behind paywalls, making it hard for organizations to measure client outcomes or understand what’s working. Finverse provides several potential solution approaches, including offering a vetted and dependable APAC public data starter kit with tools and data sets.

The prevalence of paper-based data collection, especially in rural areas: Paper-based data collection is slow, inconsistent, and prone to errors, which makes it difficult to track trends or use the information in a meaningful way. Finverse supports communities' ability to gradually shift to offline-capable or low-resourced mobile collection to reduce errors and build faster, more reliable evidence for decision making.

Inconsistent or unreliable data: Sometimes a data point as basic as “household income” can be defined inconsistently across organizations, making it nearly impossible to compare results or identify who is thriving, who’s struggling, and who’s being left out. Finverse highlights examples of financial health indicators that align with national financial inclusion frameworks to ensure comparability.

These are just three of the many challenges communities raised. And we know that if these communities had a data support and resource tool providing solutions like those above, they could do so much more to drive financial health.

Many organizations in emerging markets are still at the early stages of their data journeys. How does Finverse support capacity building in terms of building trust and governance around responsible data use?

When we talk about capacity building, we often think about technical skills. But a huge part of it is trust. For us, capacity building also means being globally informed and locally led. That shows up in how we work with communities to understand how data should be collected, accessed, and used responsibly.

A big part of that is making sure people have case studies and examples that actually apply to their context — and it’s why we work with local partners. They’re the ones with deep relationships and influence in their communities, and that’s essential when you’re trying to build trust around data, especially financial data.

When we talk about building frameworks for data and AI governance, it’s not about coming in with how things should be done. It’s about keeping communities at the table. Capacity building, in that sense, is both about strengthening technical skills and building the relationships and governance structures that help people use data responsibly.

What have you learned about the role of cross-sector collaboration in unlocking financial inclusion at scale?

Financial inclusion and financial health are huge challenges, and no single sector can tackle them alone. We need government at the table to ensure that any financial policies or innovations can actually be sustained and scaled. We need social impact organizations because they’re the ones on the ground … and they understand the local context better than anyone. And we need data experts who bring not just technical skills but a social-impact lens.

Likewise, climate shocks, pandemics, and economic crises all affect people’s financial health. We can't do it in a bubble. That's why we need all sectors to play a part in this, especially around sustainability and scale.

As more intermediaries begin using Finverse, what does success look like to you? How will you know this tool is truly helping to build more financially resilient and inclusive communities across the Asia-Pacific?

Success, for me, starts with seeing more use cases emerge, more examples of how data is being used to strengthen financial health and resilience, and to help people either enter or remain in the middle class. We also hope Finverse builds capacity so that organizations feel more confident accessing, interpreting, and applying data in their own communities. That ties directly to data.org’s broader goal of supporting one million purpose-driven data practitioners.

But the real success factor at the end of the day is about people and lives impacted. So yes, Finverse is a tool, but it’s a tool with people at the center.