AI-Powered Repayments: Lessons for Ghana MoMo Loans

Loan defaults don’t start with “bad borrowers.” They start with bad repayment systems—ones that rely on endless phone calls, brittle bank transfers, and human beings manually chasing what should’ve been scheduled and consented from day one.

That’s why Zeeh Africa’s decision to relaunch its Direct Debit product in Nigeria is more than product news. It’s a signal: digital credit in Africa is hitting a ceiling where disbursement is easy, but collection is the real work. And for Ghana—where mobile money is a daily habit and more fintechs are testing microcredit—the lesson is blunt: if repayments aren’t automated, transparent, and data-driven, defaults will keep rising.

This post sits inside our series “AI ne Fintech: Sɛnea Akɔntabuo ne Mobile Money Rehyɛ Ghana den”—how AI is making fintech and mobile money operations smarter, safer, and easier. Zeeh’s relaunch gives us a practical blueprint for what Ghana’s lenders, BNPL players, and subscription businesses should build next: AI-assisted repayment rails tied to consent, identity, and affordability.

Why digital lenders struggle with repayments (even with growth)

Digital lending fails at the same place, repeatedly: repayment discipline is operational, not motivational. Most borrowers aren’t trying to disappear; many simply juggle irregular income, multiple wallets, and competing obligations.

Nigeria’s Central Bank reported weakening loan performance in Q2 2025, with more borrowers falling behind on unsecured repayments and lenders reporting higher defaults (net balance of -1.5 in the survey). That matters for Ghana because the underlying conditions rhyme: fast-growing digital credit, thin underwriting at the low end, and high costs of collections.

The real cost of “manual collections”

Manual follow-ups create three predictable problems:

1. Collections become expensive: staffing call teams and field agents is costly, especially for small-ticket loans.
2. Good customers subsidize bad systems: higher default assumptions push up fees/interest, making responsible borrowers pay more.
3. Reputational risk explodes: aggressive or privacy-violating tactics lead to complaints, regulator scrutiny, and brand damage.

Here’s the thing about repayment: if the customer’s consent and payment rails aren’t designed upfront, you’re trying to fix it later with pressure. That’s backwards.

Zeeh’s Direct Debit relaunch: what’s actually interesting

Zeeh Africa is an open-finance startup building APIs for customer verification, bank-data access, credit analysis, and now—again—Direct Debit. They first launched Direct Debit in 2024, shut it down, then returned with a redesigned system after onboarding a beta cohort in February and growing to 22 businesses using the relaunched feature.

Direct Debit is simple in concept: a customer authorises scheduled debits from their account on agreed dates. The execution is not simple at all. The operational strength is in mandates, status tracking, retries, and handling exceptions without creating customer chaos.

Two product choices that matter for trust

Zeeh’s Direct Debit emphasises two things worth copying:

• Installments + recurring schedules that both sides agree to (useful for loans, BNPL, and subscriptions)
• Signed mandates defining amount limits and duration, with automated debits and real-time updates

That mandate-based approach aligns with Nigeria’s FCCPC July 2025 digital lending regulations, which push transparent, consented repayment methods and restrict abusive collections.

For Ghanaian operators, this is the most transferable idea: make repayment consent explicit, auditable, and revocable under clear rules. If your repayment method can’t be explained in 30 seconds to a customer, it’s probably not compliant—or sustainable.

The Ghana connection: MoMo makes repayment easier, but not automatic

Ghana’s mobile money ecosystem gives lenders something Nigeria’s bank-centric rails can’t always match: habitual daily transactions. People top up, pay bills, send money, buy airtime, and pay merchants—often multiple times a week.

So why are repayments still messy for many products?

Because MoMo-based lending often suffers from:

• Unpredictable wallet balances (income hits at irregular times)
• Multiple wallets and SIM switching
• Weak mandate design (customers “agree” in-app but don’t understand the schedule or limits)
• Limited repayment intelligence (no smart retry logic; no ability to choose the best time)

The opportunity is bigger than “add Direct Debit.” Ghana can build Direct Debit-like repayment automation inside mobile money, tied to AI models that choose timing, reduce failures, and keep consent clean.

A practical model: “Mandate + MoMo + AI”

If I were building this for Ghana today, I’d think in three layers:

1. Consent layer (Mandate)
   • customer approves amount limit, frequency, duration
   • customer gets a simple summary: “We’ll take GHS X every Friday for Y weeks”

1. Rail layer (Mobile money autopay)

   • scheduled wallet debits
   • fallback options if wallet is empty (grace period, partial payment rules)

2. Intelligence layer (AI)

   • predicts the best debit window based on inflows
   • adjusts retry strategy to reduce fees and frustration
   • flags early risk so you restructure before default

This is how “AI ne fintech” becomes real: not chatbots. Operational intelligence that reduces default without harassing customers.

Where AI actually reduces defaults (and where it doesn’t)

AI won’t magically make broke customers rich. But it will reduce preventable failures—missed timing, wrong amounts, poor communication, and slow interventions.

1) Smarter debit timing (cashflow-aware collections)

Most lenders debit on a fixed day because it’s easy for the lender. Customers don’t earn on your schedule.

AI can learn patterns like:

• salary inflows at month-end
• trader peak sales days (e.g., weekends)
• seasonal bumps (December sales, January slowdown)

In late December 2025, this is especially relevant: spending is high after Christmas, but January cashflow tightens fast. An AI-assisted system can pre-plan softer debits, partial payments, or earlier reminders during this transition.

Snippet-worthy rule: A repayment system that ignores cashflow timing creates “defaults” that are really just bad scheduling.

2) Dynamic repayment restructuring before the miss

Instead of waiting for non-payment, models can trigger an offer when risk rises:

• extend tenor by 2–4 weeks
• switch weekly to biweekly
• accept partial payments with a clear path to catch up

This works best when it’s not a negotiation circus. It should be a pre-approved playbook: the system recommends the least painful option and explains it clearly.

3) Consent-first communications (reduce disputes and complaints)

AI can tailor messages, but the bigger win is governance:

• message frequency caps
• channel rules (no embarrassing contact lists)
• plain-language explanations

If regulators in Ghana tighten digital lending rules further (a realistic direction), lenders with auditable consent and transparent repayment logic will survive. The rest will scramble.

Where AI won’t save you

AI can’t fix:

• weak identity verification
• poor affordability checks
• products priced with unrealistic repayment assumptions

That’s why Zeeh’s positioning—identity, bank data, credit insights, then payments—makes strategic sense. Collections is downstream of underwriting.

Building a repayment stack Ghanaian fintechs can defend

The Nigerian market Zeeh is entering has incumbents offering Direct Debit already. The differentiator isn’t the feature; it’s the full journey integration.

Ghana’s fintech builders should copy that logic:

The “repayment reliability stack” (minimum viable)

1. KYC + identity confidence

   • reduce duplicate accounts and synthetic identities

2. Affordability and cashflow checks

   • wallet/bank statement signals where permitted
   • income volatility scoring

3. Mandate management

   • clear limits, duration, cancellation rules
   • customer-accessible mandate history

4. Automated collections engine

   • schedules, retries, partial payment rules
   • exception handling (insufficient funds, dormant wallet)

5. AI risk operations

   • early warning triggers
   • restructure playbooks
   • cohort analytics (which products, tenors, or segments are failing)

If you’re missing #3 and #4, you’ll end up over-investing in #5 and still suffer defaults.

Quick checklist: what to implement in the next 90 days

• Write a one-page mandate summary customers can understand
• Add real-time repayment status (success, failed, retry scheduled)
• Implement smart retries (time windows + max attempts)
• Create hard rules for transparency: what you’ll debit, when, and why
• Set up early-risk triggers (late by 24–48 hours → restructure offer)

These are operational moves that reduce defaults without turning collections into intimidation.

People also ask: “Is Direct Debit safe for customers?”

Yes—when it’s mandate-based and auditable. Direct Debit becomes unsafe when customers don’t understand limits, can’t revoke easily, or lenders debit unpredictably.

For Ghana’s mobile money context, the gold standard is:

• explicit consent with clear limits
• notifications before and after each debit
• easy pause/cancel rules
• dispute resolution with a visible trail

Trust isn’t marketing. Trust is controls plus clarity.

What this means for “AI ne Fintech” in Ghana

Zeeh’s relaunch is a reminder that fintech isn’t only about new loan products. The unglamorous plumbing—mandates, rails, retries, compliance, and customer transparency—is where sustainable credit is built.

If Ghana’s mobile money lenders want lower defaults in 2026, they should stop treating collections as a call-center problem. It’s a product problem. Build consented, automated repayment first, then use AI to make it adaptive and fair.

If you’re designing a MoMo credit product, ask one hard question before launch: can your repayment system succeed without a human begging for payment? If the answer is no, you don’t have a lending product yet—you have a collections team waiting to happen.