How manual underwriting still fits into an automated loan process

Across Africa, automation has changed how lenders think about credit. Mobile-first lending apps, USSD-based products, and fully digitized bank loans have brought speed and scale that would have been impossible through paper forms and long committee meetings. A borrower who once had to queue in a branch can now get money in minutes, thanks to scoring models that pull from mobile money records, airtime purchases, or even behavioral data.

The numbers speak clearly. Safaricom’s M-Shwari in Kenya has processed loans for over 40 million customers since launch. Nigeria has seen the rise of digital lenders like FairMoney and Carbon, disbursing millions of loans every year through apps alone. These systems have made it possible to approve thousands of small loans daily, something that traditional underwriting teams could never keep up with.

But for all the progress, the story is incomplete. Automated underwriting depends heavily on structured, digital data. And in Africa, that is still a scarce resource for large portions of the population. South Africa’s TransUnion, for example, estimates that more than 16 million adults have no credit history on file. In countries without fully functional credit bureaus, that number is even higher. Informal traders, seasonal farmers, artisans, and gig workers dominate the economy, and most of them operate in cash. Their financial lives are real and active, but they do not leave the kind of footprints that algorithms are designed to read.

That is where manual underwriting still holds its ground.

How automated underwriting currently works in Africa

When lenders in Africa talk about automation, what they usually mean is that the loan decision is being handed off to a system that pulls in whatever data is available, runs it against a scoring model, and then delivers an approval or rejection. For borrowers who already have formal footprints salaried employees with pay slips, small businesses with proper bank statements, or individuals who regularly use digital channels, this tends to work fairly well. The data is structured, it can be modeled consistently, and the outcome usually makes sense.

Fintechs have pushed the boundaries further by relying on data that traditional banks never considered. Patterns in airtime recharge, call records, or mobile money usage are now used as proxies for financial behavior. Safaricom’s M-Shwari and MTN’s partnerships with credit bureaus are good examples of how telecom data has become part of credit scoring. For customers with little or no history in a bureau, this has opened the door to credit they would otherwise have been denied. It has not eliminated the challenges of thin files, but it has made the first-time approval rate stronger than it used to be.

Microfinance banks and SACCOs are also moving in this direction, though in a way that blends new tools with old practices. In Nigeria, for instance, many microfinance lenders now digitize applications, run automated checks, and use decisioning engines for smaller exposures. When the numbers get bigger, they still hand the case to a credit officer for a closer look. What automation has really changed here is the efficiency of routine tasks. Things like verifying phone numbers, calculating simple ratios, or cross-checking with blacklists no longer consume as much human time. That frees credit teams to spend their energy on the parts of lending that actually require judgment.

Also read: Breaking down the 3 R’s of credit and why they still matter

Where manual underwriting still holds its ground

For all the gains of automation, there are still moments when a lender knows the system’s verdict alone is not enough. This usually happens when the borrower’s financial story doesn’t follow a neat digital trail. Across African markets, this is common. Many borrowers operate in the informal sector where income is cash-heavy, irregular, or seasonal. A market trader in Accra may have significant earning power, but their digital footprint often tells only a fraction of their financial reality.

This is where manual underwriting comes in. A credit officer or underwriter may need to call references, visit a business site, or review collateral documentation that no algorithm can fully interpret. Reports have shown that a large share of small business owners in sub-Saharan Africa still struggle to access formal credit because their records cannot be digitized into simple scoring models. For lenders willing to take that extra step, manual review becomes the difference between rejecting an applicant outright and responsibly approving a loan with the right level of risk controls.

Manual underwriting also helps lenders protect their portfolios when borrower behavior looks unusual. Automated systems may flag a customer for rejection if their mobile wallet activity suddenly drops, but a human reviewer might uncover that the borrower has simply shifted funds into a cooperative savings scheme that doesn’t show up in digital transaction logs. Without that context, a perfectly good customer would have been turned away.

Even with salaried workers who appear straightforward, manual checks remain valuable. In Kenya, for instance, lenders often double-check payslips against HR records to guard against forged documents. The system can run the numbers, but a human review validates the source. The same is true for collateral-backed lending, where title deeds, vehicle registration, or land documents need verification beyond what a scoring engine can provide.

The risks of leaning too heavily on one side

African lenders who put all their weight on automation risk excluding large segments of potential borrowers whose financial activity cannot be fully captured by data pipes. The outcome is missed business opportunities and an overdependence on thin credit files that may not tell the whole truth. On the other hand, lenders who stick stubbornly to manual processes end up limiting their ability to scale. They become slow, operationally heavy, and unappealing to borrowers who expect faster service.

The real danger is not picking automation over manual, but failing to find the balance that reflects the realities of the market a lender is serving.

What lenders should really be thinking about

Instead of framing the question as “automated or manual,” the smarter discussion is about context. Who is your core borrower base? What kinds of risks are most common in your portfolio? Which parts of underwriting can technology handle reliably, and which parts need human intervention? A digital-first lender in South Africa targeting salaried workers with strong bureau data might lean 90% on automation. A microfinance institution in Uganda serving informal traders may require far more manual review to supplement automated scoring.

The trick is to design an underwriting workflow that blends both strengths. Automation should handle speed, scale, and consistency. Manual review should step in for edge cases, irregular income patterns, and document verification that technology still struggles to decode.

Also read: 3 alternative data to credit report for enhancing underwriting quality

The future is underwriting that delivers quality

For lenders in Africa, the future will belong to those who use automation and manual review as complements, not competitors. A good underwriting system should be smart enough to process a majority of loans digitally but flexible enough to flag applications for deeper review when needed. That balance doesn’t just protect the lender’s capital, it expands access to credit for borrowers who might otherwise be left behind.

Technology will keep advancing, and more data sources will eventually make automation stronger. But for now, the African lending environment still demands the human touch. The lenders who win will be those who know when to trust the system and when to pick up the phone, visit the business site, or demand that extra layer of verification.

This is exactly the kind of balance Lendsqr’s technology was built for. Our underwriting tools give lenders the best of both worlds: automated decisioning that saves time, with room for manual checks when the stakes are higher. Whether you’re just starting out or not, Lendsqr helps you build an underwriting process that is both smarter and safer. Book a demo to learn more.