AI-Driven Direct Debit: Reduce Loan Defaults Fast

Loan defaults don’t usually start as “fraud.” Most times, it’s plain friction: salary delays, MoMo wallet emptying out faster than planned, a borrower who intended to pay but forgot, or a lender whose reminders arrive too late. Nigeria’s Central Bank flagged weakening loan performance in Q2 2025, and digital lenders reported higher defaults on unsecured loans (net balance: -1.5 in the survey). That number isn’t just a statistic—it’s a signal that collections operations are now as critical as underwriting.

That’s why Zeeh Africa’s decision to relaunch Direct Debit is bigger than a product update. It’s a clear admission of something most fintechs hate to say out loud: disbursing loans is easy; collecting them is the hard part. And for our series, “AI ne Fintech: Sɛnea Akɔntabuo ne Mobile Money Rehyɛ Ghana den,” Nigeria’s story reads like a preview of what Ghanaian lenders, MoMo-based credit products, and subscription businesses will face as digital credit scales.

Here’s the stance I’ll take: defaults rise when repayment is treated as a “customer behaviour problem” instead of a “system design problem.” Automation—especially consent-based, well-instrumented direct debit—fixes the system. Add AI on top, and you don’t just collect better; you lend smarter.

Why defaults rise when repayment stays manual

Answer first: Manual collections creates delays, inconsistency, and misinformation—three ingredients that inflate default rates even when borrowers are willing to pay.

Zeeh’s CEO captured an uncomfortable truth: many lenders still rely on follow-ups, reminders, and “promise to pay” cycles. That approach breaks at scale because it depends on human attention and perfect timing. It also turns collections into a noisy negotiation instead of a predictable process.

A manual repayment setup typically fails in predictable ways:

• Timing mismatch: Borrowers get paid on specific days, but reminders don’t align with cash availability.
• Payment rail failure: Transfers fail, bank apps are down, or customers can’t complete steps.
• Operational overload: As portfolios grow, collections teams either balloon (costly) or fall behind (defaults).
• Portfolio distortion: Good borrowers get priced like risky borrowers because repayment data is messy.

For Ghana, the parallel is obvious. Many credit products ride on mobile money or card rails. When repayment is “pay us back when you can,” lenders get volatility. When repayment is “pay on an agreed schedule, with consented automation,” lenders get predictability.

What direct debit actually fixes (and what it doesn’t)

Answer first: Direct debit improves repayment execution—it doesn’t magically fix poor underwriting. But it gives lenders cleaner repayment data and fewer avoidable misses.

Direct Debit is simple in concept: a customer authorises a lender to debit an account on agreed dates. Zeeh’s relaunch emphasises two practical parts that matter:

1. Structured instalments and recurring transactions that both sides consent to.
2. Signed mandates that specify amount, frequency, and time window—then the system schedules debits and returns status updates.

That second part—mandates—matters more than most people think. A mandate forces clarity:

• How much can be taken?
• On what dates?
• For how long?
• What happens if a debit fails?

When that’s defined upfront, collections becomes less confrontational and more procedural.

What direct debit does not do:

• It won’t make an unaffordable loan affordable.
• It won’t fix a lender that approves customers with unstable cashflows.
• It won’t solve disputes unless the lender has transparent policies.

But here’s why I still like it: direct debit turns repayment into infrastructure. And once repayment is infrastructure, AI can optimize it.

The AI layer: from “collecting” to “preventing” defaults

Answer first: AI reduces defaults by predicting repayment risk early, selecting the right repayment method, and timing debit attempts to real cashflow.

Zeeh positions itself as an “open-finance” infrastructure provider: identity checks, access to bank data, credit-risk analysis, and now direct debit. That stack is powerful because repayment is downstream of data. If you can see behavioural signals (income cadence, balance volatility, prior repayment history), you can intervene before a customer becomes delinquent.

Three AI use-cases Ghanaian lenders can copy immediately

1) Pay-date alignment (cashflow timing model) If salary hits on the 25th and school fees land on the 1st, your “every 30 days” schedule might be wrong. An AI model can detect income cycles and recommend:

• debit on salary day + 24 hours buffer,
• split instalments (60/40) across two dates,
• smaller weekly debits instead of one monthly hit.

2) Smart retry strategy (failure-aware collections automation) A single failed debit shouldn’t trigger harassment or a default label. AI can classify failures:

• insufficient funds vs technical failure vs account restriction,
• pick the best retry window,
• send the right message (not the same nagging SMS).

3) Early-warning risk scoring (behaviour drift detection) Risk isn’t static. AI flags drift like:

• income drops for 2 months,
• balance swings widen,
• wallet cash-outs spike,
• repayment habit changes.

That enables pre-emptive actions such as restructuring, partial payment plans, or temporary limits.

A snippet-worthy line that holds up in board meetings:

Collections works best when it’s a product feature, not an emergency response.

Compliance isn’t optional—mandate-based repayment is the safer path

Answer first: Consent-based direct debit lowers regulatory and reputational risk because it replaces aggressive collections with transparent, auditable repayment.

Nigeria’s FCCPC introduced digital lending regulations in July 2025 that push for transparent, consented repayment methods and clamp down on privacy-violating collection practices. Zeeh explicitly frames its mandate flow as aligned with that direction.

Ghana’s market isn’t identical, but the principle travels well: regulators tighten rules when consumers get hurt. If your collections strategy depends on shame, threats, or loose data handling, you’re building on sand.

Mandate-based repayment supports a cleaner compliance posture:

• Clear customer consent captured and stored
• Defined limits (amount/time) to prevent abuse
• Audit trails for disputes
• Less dependence on intimidation as an “operational tool”

For fintechs trying to win enterprise partners (schools, merchants, payroll providers), compliance isn’t just legal—it’s commercial.

Lessons for Ghana’s mobile money and digital lending ecosystem

Answer first: Ghana’s scale will come from combining MoMo convenience with bank-grade repayment controls and AI-driven credit operations.

Ghana is already comfortable with mobile money for everyday payments. The next leap is making credit operations as smooth as MoMo transfers—especially for:

• micro-loans tied to merchant sales,
• salary-advance products,
• school-fee financing,
• pay-later for devices and inventory,
• subscription services (internet, learning apps, utilities).

Here’s what Nigeria’s direct debit push signals for Ghana:

1) Repayment rails should match customer reality

Some customers keep value in MoMo, not bank accounts. Others receive salary via bank but spend via wallet. The winning strategy is multi-rail repayment:

• MoMo autopay/standing instructions where available
• bank direct debit mandates for banked customers
• card-on-file for specific segments

The point isn’t to pick one rail. It’s to pick the rail that reduces “repayment effort.”

2) Underwriting and repayment must be designed together

Most lenders separate them: the credit team approves; operations chases later. That’s backwards.

A better workflow is:

1. Verify identity (strong KYC)
2. Assess affordability (cashflow + obligations)
3. Offer a repayment plan the customer can actually maintain
4. Collect using consented automation
5. Continuously re-score risk as behaviour changes

That’s the “AI ne fintech” theme in practice: automation across the whole journey, not only at onboarding.

3) Better repayment data improves pricing and inclusion

When repayments are captured cleanly and consistently, lenders can:

• reduce interest for reliable customers,
• increase limits responsibly,
• approve thin-file borrowers using behaviour signals,
• stop punishing good customers for the portfolio’s noise.

Financial inclusion isn’t only “more loans.” It’s fairer loans—priced and structured based on real repayment capacity.

Practical checklist: how to implement automated repayment without harming trust

Answer first: Start with consent, clarity, and customer control; then add AI optimization.

If you’re building or upgrading a loan product (or any recurring payment product) in Ghana, this checklist keeps things practical.

Step 1: Build a mandate customers can understand

• Use plain language (no legal fog)
• Show total repayment amount and schedule
• Allow customers to see upcoming debits
• Provide a simple cancellation/dispute path

Step 2: Design for partial payment, not punishment

Real life is messy. Your product should handle it.

• Allow split payments
• Support grace periods
• Offer restructuring before delinquency

Step 3: Make debit attempts “cashflow-aware”

Even a basic rules engine helps:

• debit after typical pay times
• avoid days with high bill pressure
• retry using smarter windows, not random retries

Step 4: Automate communications like a grown-up brand

• confirm mandates instantly
• notify before each debit
• explain failures clearly
• avoid threatening language

Step 5: Add AI where it measurably reduces defaults

Good first models:

• likelihood-to-pay score
• optimal debit day prediction
• failure reason classification

If the model can’t change an action, don’t build it yet.

People also ask (and the straight answers)

Is direct debit safe for customers?

Yes—when it’s mandate-based, transparent, and limited by amount and duration. The danger comes from vague authorisations and poor dispute handling.

Will automation increase repayment rates?

Automation increases on-time execution and reduces “forgotten” or “hard-to-pay” cases. But if loans are unaffordable, defaults still happen—just more predictably.

What’s the difference between direct debit and standing order?

Direct debit is initiated by the business (with consent). A standing order is typically initiated and controlled by the customer. Businesses prefer direct debit for reliability and status reporting.

The real opportunity: collections as infrastructure, powered by AI

Zeeh is entering a crowded direct debit market with incumbents already offering similar capabilities. The differentiator they’re betting on—bringing identity, bank data, risk analysis, and repayment into one layer—is exactly the direction Ghanaian fintech should be thinking.

If your loan product still depends on manual reminders and hope, you’re budgeting for defaults. If your repayment is consented, automated, and improved with AI-driven timing and risk signals, you’re building an asset: a repayment engine that gets stronger with every cycle.

For the AI ne Fintech series, this is the bigger lesson: automation isn’t about replacing humans; it’s about removing the avoidable reasons people fail. The next wave of fintech winners in Ghana won’t be the loudest marketers. It’ll be the teams that treat repayment as a product, use mobile money and bank rails intelligently, and apply AI to prevent delinquency before it shows up in the numbers.

So here’s the forward-looking question worth sitting with: when digital credit scales another 2–3x in Ghana, will your repayment system scale with it—or will defaults be the tax you pay for growth?