The global digital lending market was valued at $507 billion in 2025 and is projected to reach $566 billion in 2026, with AI-driven underwriting processes now controlling 43% of digital lending decisions, up sharply from three years ago. In Nigeria, the Philippines, Indonesia, and other emerging markets, the opportunity is larger still: fewer than one in twenty Nigerian MSMEs currently have access to bank credit, and Indonesia has over 65 million MSMEs comprising 97% of total employment; the vast majority of them still underserved by formal credit.
But the opportunity and the difficulty are the same thing. The borrowers that represent the largest market, which includes gig workers, small traders, SME owners, and first-time borrowers, are also the ones that traditional credit infrastructure cannot assess, acquire, or serve efficiently. Meaning winning digital lending customers in the AI-era could simply be a case of getting infrastructure and data as good as possible and the rest flowing from there.
This guide covers the five areas where digital lenders in emerging markets are winning or losing customers in 2026:
- Finding borrowers traditional credit misses
- Designing lending journeys that convert
- Making faster credit decisions
- Retaining borrowers after the first loan
- Building the infrastructure that makes all of it possible at scale
Find the borrowers traditional credit misses
One of the single largest opportunities in digital lending in emerging markets is building the infrastructure required to serve customers who do not have credit scores.
McKinsey research shows that expanded credit access through alternative data could add $3.7 trillion to emerging market GDP by 2030. The mechanism is straightforward: millions of creditworthy borrowers are currently invisible to traditional scoring models because they lack formal credit histories, have irregular income, or primarily transact in cash. Lenders that can see these borrowers and assess their creditworthiness by using non-traditional signals have access to a customer segment that their competitors cannot reach.
Alternative data for credit assessment has moved from experimental to mainstream in emerging markets. The AFI 2025 report on alternative data for credit scoring confirms that digital lenders in emerging markets are adopting these data sources at scale. The signals that matter most in these markets include the following categories.
Mobile money and digital wallet transaction patterns
Consistent mobile money activity, which includes regular deposits, bill payments, and remittances, is a strong predictor of credit behaviour even in the absence of a formal credit file. In Nigeria and the Philippines, where mobile money penetration is high, this data is rich and accessible through API integrations.
Utility and telecom payment consistency
Utility payment consistency and telecommunications bill payments provide particularly reliable credit signals in emerging markets. Paying the electricity bill and phone contract on time, consistently, over twelve months can be a meaningful indicator of financial reliability.
Business transaction history
For SME borrowers, bank statement data and payment platform records provide a direct view of cash flow which can be more predictive than balance sheet ratios for small businesses that may have informal accounting.
Behavioural signals
Device usage patterns, app engagement consistency, and digital footprint signals are increasingly used by lenders in Southeast Asia and Sub-Saharan Africa to supplement traditional assessment, particularly for first-time borrowers with no prior credit history.
The infrastructure requirement for leveraging these signals is a core banking system that can ingest, process, and combine data from multiple external sources through open APIs, as well as a data layer that makes that combined view accessible to credit decisioning models in real time.
Design lending journeys that convert
Acquiring a potential borrower’s attention is not the same as converting them into a funded customer. The gap between application and disbursement is where most digital lenders lose the customers their marketing spent money to reach.
Customer abandonment rates exceed 50% when account or application processes take more than three to five minutes. For lending specifically, the friction points are well understood:
- Document upload requirements that don’t work on older smartphones
- KYC verification that requires a branch visit
- Unclear status updates that leave applicants uncertain whether to wait or give up
- Disbursement that takes days when competitors are achieving disbursement in only hours
The lending journeys converting at the highest rates in dynamic markets in 2026 share several characteristics, including the following.
Mobile-first, low-data design
The borrower applying for a working capital loan in Lagos or Cebu is likely doing so on a mid-range Android device with variable connectivity. Lending flows optimised for this reality are fast-loading, low-data, with session persistence if connectivity drops and can convert at higher rates than those designed for desktop or high-speed connections.
Progressive document collection
Asking for everything upfront feels like a bureaucratic process. Collecting documents progressively, at the point in the journey where each is contextually relevant, reduces abandonment. The goal is to make each step feel like a natural next thing to do, not an obstacle to clear.
Real-time KYC
Integrated e-KYC with biometric verification, where regulations permit, removes the waiting period that causes most abandonment between application and approval. The integration of digital identity verification has been shown to reduce onboarding drop-off rates by 31%. In Nigeria, real-time BVN and NIN validation is now a CBN requirement and lenders that have automated this remove a friction point that competitors with manual processes still carry.
Instant status updates
Any borrower who submits an application and hears nothing for 24 hours has almost certainly started exploring alternatives. Automated, real-time status notifications at every step of the process reduce both abandonment and inbound support contacts simultaneously.
Clear terms, no surprises
Transparency in lending terms, where users can clearly see the exact cost of the loan, the repayment schedule, and the consequences of late payment, is both a regulatory expectation and a conversion driver. Borrowers who encounter unexpected fees or unclear terms after disbursement do not become repeat customers. Those who understand exactly what they agreed to before signing are significantly more likely to repay on time and borrow again.
Make faster credit decisions
Speed is one of the dimensions of credit decisioning that matters most to borrowers in emerging markets. A small trader who needs working capital to restock inventory before the weekend market cannot wait three days for a credit decision and a gig worker whose vehicle needs repair cannot wait a week. The value of fast credit is directly proportional to the borrower’s time-sensitive need.
AI-driven credit scoring systems improve default prediction accuracy by 15 to 25% compared to legacy methods, while enabling lenders to approve more loans to qualified borrowers and reduce default rates by as much as 30%. The speed and accuracy gains come from the same source: the ability to analyse a much broader dataset than a human underwriter could, and to update risk models dynamically as new data arrives.
The practical implications for lenders in emerging markets include the following.
Real-time decisioning requires real-time data
A credit decision made on yesterday’s transaction data is less accurate than one made on today’s. Now imagine you’re dealing with truly historic data. The lenders making the best decisions the fastest are those with real-time access to the data their models need, such as transaction histories, behavioural signals, bureau data, and alternative data, and are then pushing all that through a unified data layer that is continuously updated.
Loan decisions can reflect actual behaviour, not proxies
For SME borrowers, cash flow analysis from bank statement data is a more direct and more current indicator of credit capacity than financial statements that may be months old. For individual borrowers, transaction patterns from the past 90 days tell a more accurate story than a static credit file updated annually. Integrating cash-flow analytics enhances the accuracy of credit risk models and expands access to finance for underserved consumers, according to the Bank for International Settlements.
Renewal decisions can be almost instant
For borrowers with a repayment history on the platform, renewal decisions should require almost no new underwriting. The data is already there. Lenders that make borrowers re-apply with the same documentation for every loan cycle are creating unnecessary friction and giving competitors an opening. The right model recognises that a borrower who repaid three consecutive loans on time is a known quantity and treats them accordingly.
Retain borrowers after the first loan
Winning a borrower’s first loan is the beginning of the customer relationship, not the end of the acquisition process. More than 78% of SMEs worldwide now use digital-only banking solutions, and those that find a lender they trust tend to deepen that relationship, doing things like increasing loan sizes, referring other businesses, and moving more of their financial activity to the same institution.
Retention in lending is determined by three things:
- Whether the borrower’s experience matched their expectations
- Whether the renewal process is frictionless
- Whether the lender demonstrates that it understands the borrower’s business over time
Expectation management starts at origination
A borrower who understood exactly what they were agreeing to is far more likely to repay on time and far more likely to return. Lenders with high repeat borrower rates consistently invest in the clarity of their origination communication, not just legally compliant disclosures, but genuinely clear explanations of cost and terms in the borrower’s language and context.
Renewal should be easier than the first loan
A borrower who repays on time should find the renewal process noticeably simpler than the original application. Pre-populated forms, reduced documentation requirements, and faster decisions for known customers are all achievable on a modern core banking platform and are consistently correlated with higher repeat borrowing rates.
Proactive engagement between loans
Monitoring the signals that indicate when working capital needs are likely to arise, through things like seasonal patterns in transaction data, inventory reorder cycles visible in payment flows, revenue dips that precede liquidity needs, and reaching out proactively can enable you to retain more SME borrowers. Banks using AI for personalised insights achieve a 12.3% higher retention rate than those that do not.
Collections that preserve the relationship
For borrowers who miss payments, the quality of the collections process determines whether the relationship survives. Automated early warning systems that flag at-risk borrowers 30 to 60 days before delinquency, based on changes in transaction patterns, declining balances, or reduced platform activity, allow lenders to reach out with support before a payment is missed rather than enforcement after it is. This approach is better for portfolio quality and better for the borrower relationship simultaneously.
Build the infrastructure that makes it all possible at scale
The four capabilities above all depend on the same underlying infrastructure. Succeeding in 2026 and beyond in emerging markets requires building or choosing infrastructure that provides three things:
- Complete, real-time data about your borrowers
- The ability to configure and update credit products quickly without vendor dependency
- The scalability to handle growth without performance degradation
A governed data layer
The fundamental data requirement for modern digital lending is a complete, continuously updated view of each borrower across every product and channel. Transaction history, repayment behaviour, support contacts, mobile wallet activity, and external data signals all need to be accessible from a single source, in near real-time, for the credit decisioning models that drive the lending business. Institutions still working from batch exports or fragmented system-by-system data cannot build AI credit models that perform well, cannot make real-time decisions, and cannot identify at-risk borrowers early enough to intervene effectively.
Configurable products without vendor cycles
Credit product design in emerging markets requires iteration. Interest rates, repayment schedules, eligibility criteria, and fee structures all need to be adjustable based on portfolio performance, regulatory changes, and competitive dynamics. If adjusting a product parameter requires raising a vendor ticket and waiting weeks for a scheduled release, the lending operation is perpetually behind the market. Modern, API-first cores allow product and risk teams to adjust these parameters through configuration, in days rather than months.
Scalability that doesn’t require manual intervention
Digital lending growth often comes in spikes driven by payroll cycles, campaign periods, and partnership launches. Infrastructure that requires manual scaling procedures or degrades under peak load creates exactly the kind of failure at high-value moments that drives borrowers to competitors.
Building scalable growth in Nigeria, the Philippines, and Indonesia is not necessarily a case of having the biggest marketing budget or the most sophisticated AI model. Instead, it is often achieved by leveraging infrastructure that enables you to act on what you know about your borrowers quickly, such as approving the right credit at the right moment, retaining customers who deserve retention, and growing without the systems beneath you becoming a bottleneck.
Oradian: The core banking system for sustainable growth
For more on the infrastructure and data foundations behind sustainable digital lending growth, read the Oradian Growth Playbook for Banks.
To understand how AI can be deployed effectively in credit decisioning, fraud detection, and customer retention, read The Digital-First Bank’s Guide to AI in 2026.
And to start leveraging the core banking system built for digital banks and lenders in emerging markets, contact vanda.jirasek@oradian.com for a commitment-free demo today.
