Direct Debit ne AI: Sika Saneɛ Ma Credit Tu Dɛn

Loan defaults no nyɛ “collections team no ntumi nnyɛ adwuma” asɛm kɛkɛ. Mpɛn pii no, ɛyɛ payments infrastructure asɛm. Wode loan bɛma obi ntɛm—mobile money anaa bank transfer—nanso sɛ bere a ɛsɛ sɛ wokɔgye sika no a, na wo system no gyina manual reminders, “mepaakyɛw” promises, ne transfers a ɛtɔ da a ɛnni mu a, na w’aka ho asɛm.

Ɛno nti na me de Nigeria mu Zeeh Africa Direct Debit relaunch no reba mu wɔ yɛn “AI ne Fintech: Sɛnea Akɔntabuo ne Mobile Money Rehyɛ Ghana den” series yi mu. Nigeria mu nsɛm no kyerɛ yɛn ade koro: automation a wɔde mandate (consent) so yɛ kwan a ɛbɛma digital lending ne subscriptions (te sɛ school fees financing, BNPL, utilities) agyina pintinn—na ɛbɛtumi ayɛ adwuma wɔ Ghana mobile money ecosystem mu nso.

Adɛn nti na defaults reforo—na adɛn nti na automation yɛ answer

Answer first: Defaults reforo bere a credit access reyɛ mmerɛw sen repayment collection. Sɛ repayment no nyɛ “built-in” wɔ payment rail no mu a, wobɛda ho.

Nigeria Central Bank Credit Conditions Survey (Q2 2025) kyerɛɛ sɛ unsecured loan repayments mu nsɛnnennen rekɔ so, na lenders report default rates a ɛrekɔ soro (net balance -1.5). Eyi nyɛ Nigeria nkutoo. Ɛyɛ pattern a yɛahu wɔ markets a:

• digital lenders dodow reforo ntɛm
• underwriting (credit scoring) reyɛ yiye kakra, nanso repayment operations still yɛ manual
• borrowers kura accounts pii (bank + mobile money), na payment habit no yɛ inconsistent

Ghana mu nso, mobile money ama disbursement ayɛ “tap-tap”. Nanso collections a ɛyɛ dwoodwoo no kɔ so de adwumayɛfoɔ dodow bɔ mu, na ɛma cost per loan sɔre. Afei default ba a, lenders taa fa “risk” no gu interest rates so. Ɛno na ɛbɔ good borrowers asu—na ɛma credit yɛ den ma wɔn a wɔyɛ disciplined.

One-liner: “Sɛ wotumi tua loan no ntɛm a, nanso wuntumi nnye repayment no ntɛm a, w’ahyɛ software problem bi mu.”

Direct Debit yɛ dɛn—na dɛn na Zeeh resiesie

Answer first: Direct Debit yɛ agreed, automated debit a customer de mandate (consent) ma, na system no gye sika no wɔ da a wɔahyɛ.

Zeeh Africa (open-finance startup) relaunchɛ Direct Debit no bere a defaults reforo. N’adwene no yɛ straightforward: ma repayment nyɛ “chasing people” adwuma, na mmom scheduled collection a wɔde transparency ne consent to mu.

Ɛnyɛ “force debit”—mandate-based, clear rules

Direct Debit pa no nyɛ sɛ lender bɛtumi “fa sika biara” fi account bi mu. Mandate no kyerɛ:

• amount a wɔbɛtumi adraw
• frequency (weekly/monthly)
• duration (3 months, 6 months…)
• reference (loan ID, subscription plan)

Zeeh de signed mandates ne real-time status updates bɔ mu. Ɛno ma dispute handling, reconciliation, ne customer trust yɛ mmerɛw.

Adɛn nti na Zeeh’s approach ho hia

Incumbents pii wɔ Direct Debit already. Nanso Zeeh repɛ advantage a ɛfa full credit journey ho: KYC, bank data access, affordability checks, statement analysis, credit insights, na afei repayment automation—all under one layer. Sɛ lender no anyɛ provider 3–5 stitching a, operational errors tew.

Ghana mu: dɛn na yɛbɛtumi asua—mobile money + open banking + AI

Answer first: Ghana bɛtumi anya repayment improvements sɛ lenders de mandated recurring payments bɔ mobile money rails ho, na wɔde AI ma onboarding, affordability, ne early-warning alerts.

Nigeria’s lesson no nyɛ “copy Zeeh.” Ɛyɛ copy the logic: payments must be designed for repayment from day one.

1) Repayment design: “pay-in” vs “pull” (debit)

Ghana mu lenders pii yɛ “pay-in”—borrower no na ɛsɛ sɛ ɔkɔtua (MoMo push, bank transfer). Eyi yɛ risky efisɛ:

• borrower bɛtumi afrɛ sɛ “m’ani agye a mɛtua”
• salary day mu cashflow bɛkɔ utilities/food
• friction (USSD failures, app downtime) bɛma delay

Direct Debit yɛ “pull”—system no na ɛgye sika no wɔ schedule so. Sɛ customer consent wɔ hɔ a, repayment behaviour kɔ soro.

2) Open banking + mobile money: creditworthiness a ɛyɛ realistic

Open banking (anaa open finance) kyerɛ consented data sharing: bank statements, transaction history, recurring inflows, obligations. Ghana mu, sɛ lender tumi hu:

• salary patterns (da a sika ba)
• spending volatility
• existing loan deductions

a, underwriting yɛ accurate, na installment schedule no bɛyɛ “fit” ma customer no.

3) AI wɔ he? Ɛnyɛ buzzword—ɛyɛ operational math

AI/ML boa wɔ repayment system mu wɔ 3 places a ɛyɛ practical:

1. Affordability forecasting: model a ɛka “sɛ yɛde installment yi to customer yi so a, default probability bɛyɛ dɛn?”
2. Dynamic scheduling: instead of fixed 30th, system no tumi suggest “2 days after salary inflow” based on history.
3. Early-warning signals: sɛ inflows retew, wallet balance low, anaa unusual spending spikes a, system no tumi de gentle nudges (not harassment) bɔ borrower no ho ban.

Point no: AI no si payment automation so dua. Sɛ wunnya mandate-based rail a, AI alerts bɛyɛ “nice-to-have” kɛkɛ.

Compliance ne trust: the rulebook that protects both sides

Answer first: Automated debits must be transparent, consented, auditable—otherwise it becomes abuse, and regulators will clamp down.

Nigeria mu, FCCPC July 2025 regulations de emphasis sii transparent repayment methods so, na ɛbraa aggressive collection practices ase. Eyi yɛ signal kɛse ma Ghana fintechs: growth a ɛda so no bɛkɔ so only if customers trust the system.

Sɛ wo business wɔ Ghana (digital lender, BNPL, school fees platform, subscription business) na wopɛ Direct Debit-style flow a, fa these principles yɛ standard:

• Mandate clarity: show amount, dates, duration in plain language (Twi/English)
• Easy cancel/modify rules: customer nhwehwɛ “office visit” ansa na wɔatwe mandate no
• Receipts + status updates: every debit attempt should have a clear success/fail message
• Data minimization: collect only what’s needed for underwriting + repayment
• Dispute workflow: clear SLA for reversals/complaints

Trust no nyɛ PR. Ɛyɛ system design.

Practical playbook: sɛ wokɔ build anaa wokɔ buy Direct Debit for Ghana

Answer first: Start with one high-impact use case (loans or subscriptions), build mandate + reconciliation first, then layer AI and open finance.

Here’s a rollout plan a mehu sɛ ɛyɛ workable (especially for SMEs ne mid-sized fintechs):

Step 1: Pick a “repeatable payment” product

Direct Debit works best where payments are recurring:

• micro-loans with weekly/monthly installments
• BNPL repayments
• school fees financing schedules
• insurance premiums
• gym/streaming/subscription services

Step 2: Make mandate collection part of onboarding

Don’t treat it as an afterthought. During signup:

1. Explain repayment schedule
2. Capture consent (digital signature/OTP)
3. Store mandate details and audit trail

Step 3: Build strong reconciliation (before fancy AI)

Most teams skip this and suffer later. You need:

• unique reference IDs per installment
• real-time status: pending, success, failed, reversed
• automated retries with limits (e.g., max 2 retries)

Step 4: Add AI where it reduces cost fast

The first AI features that pay for themselves:

• probability of default scoring per customer
• best-day-to-debit recommendation
• segmentation for reminders (gentle, compliant nudges)

Step 5: Measure what matters (not vanity metrics)

Track:

• Repayment rate (on-time vs late)
• Cost of collections per active loan
• Failed debit rate (by network/rail/time)
• Customer complaints per 1,000 debits

If those improve, your credit product becomes cheaper and more scalable.

Ghana’s opportunity: build credit that doesn’t punish good borrowers

Defaults no ma interest rates kɔ soro, na ɛma disciplined customers tua “bad debt tax.” Automated repayments—especially mandate-based Direct Debit tied to real consent—yɛ one of the few fixes a ɛma system no yɛ fair.

Nigeria’s Zeeh story kyerɛ sɛ fintech growth no fa infrastructure choices so. Disbursement is easy. Collections is the hard part. Sɛ Ghana fintech ecosystem de mobile money strength, open finance data, ne AI-driven risk models bɔ mu a, yɛbɛtumi ama digital credit ayɛ sustainable—na ɛnyɛ “high interest, high stress” market.

Sɛ woyɛ lender anaa subscription business wɔ Ghana a, hyɛ wo ho mmɔden ma repayment automation nyɛ core product requirement, na ɛnyɛ feature a wobɛbɔ ho akyiri. 2026 rekɔba, na customers bɛkɔ brand a ɛma wɔn tua wɔn ho ka wɔ kwan a ɛyɛ clear, respectful, na ɛyɛ predictable.

Wobɛpɛ sɛ wo business no bɛyɛ “pay-in” business anaa “mandate-based repayment” business—na dɛn na wobɛyɛ seesei ansa na defaults no mmɔ wo margin ase?