AI-Powered Fintech Growth: Lessons for Cameroon

Fintech isn’t just “doing well” in Africa—it’s dominating the growth charts. The Financial Times and Statista’s latest ranking of Africa’s fastest-growing companies (measured by revenue CAGR from 2020 to 2023) shows 7 of the top 20 are fintechs. That’s 35% of the continent’s fastest growers packed into one sector.

If you work in telecoms or fintech in Cameroon, that stat should feel personal. Cameroon’s mobile-first economy is built on distribution (agents), trust (KYC), reliability (networks), and speed (instant payments). Those are the same fundamentals that helped fintechs in Nigeria, Morocco, South Africa, Zambia, Uganda, and Rwanda scale fast. The difference is how aggressively the leaders are using AI automation—not as a buzzword, but as a way to run operations, reduce fraud, and sell to millions without hiring thousands.

This post is part of our “How AI Is Transforming Telecommunications and Fintech in Cameroon” series. The goal here is simple: use Africa’s fastest-growing fintechs as a mirror, then extract practical AI moves a Cameroonian operator, wallet, aggregator, or fintech team can implement in weeks—not years.

What the “fastest-growing fintechs” are really telling us

The headline is about rankings, but the real signal is the operating model.

The ranking tracks compound annual growth rate (CAGR) of revenue between 2020 and 2023 and includes 130 companies across Africa. Within that list:

• 26 companies come from fintech, finance, and insurance
• 14 are fintechs (about 10.8% of the full ranking)
• 7 fintechs land in the top 20, with annual growth rates between 151.2% and 583.6%

Here’s what that implies: fintechs are scaling because they’ve become excellent at two things—distribution and decisioning.

Distribution means reaching customers cheaply (apps + agents + partnerships). Decisioning means making fast, repeatable choices at scale (credit approvals, fraud checks, customer support routing, personalized offers). AI is the accelerant for decisioning, and telecom infrastructure is often the accelerant for distribution.

For Cameroon, this matters because the most valuable wins usually sit at the overlap:

• Telcos expanding beyond connectivity into financial services
• Fintechs borrowing telco tactics (agent networks, airtime-like micropayments)
• Both relying on AI to keep unit economics healthy while scaling

The top 7 performers—and the AI patterns hiding in plain sight

These companies weren’t ranked for “best AI.” They were ranked for revenue growth. But if you look at their products, you can spot the AI-shaped patterns: automation, personalization, and risk control.

PalmPay (Nigeria): scale comes from transactions, not branding

PalmPay ranked #2 overall with an annual revenue growth rate of 583.6%, growing revenue from US$200,000 (2020) to US$63.9 million (2023)—an absolute growth of 31,850%. It reports 35 million+ app users and up to 15 million transactions daily.

The AI lesson: when you’re processing millions of small-value transactions, manual review collapses. You need AI-driven monitoring to:

• flag abnormal transaction patterns in real time
• score device and account risk
• reduce false declines while still blocking fraud

Cameroon application: if you run a wallet, aggregator, or telco mobile money product, set up a simple “fraud stack” roadmap: rules first, then machine learning scoring, then real-time streaming decisions. The biggest ROI usually comes from reducing:

• fraudulent cash-outs
• account takeovers
• agent-based collusion patterns

eShandi (Zambia): last-mile inclusion needs automated underwriting

eShandi ranked #4 overall with 276.4% annual growth, from US$120,000 to US$6.47 million (2020–2023). It focuses on instant, collateral-free digital loans and last-mile services for underserved communities.

The AI lesson: inclusion at scale depends on alternative data underwriting and continuous learning. Traditional credit bureau signals are thin for many customers. AI helps turn weak signals into usable risk decisions.

Cameroon application: lending products in Cameroon can use AI responsibly by starting with transparent scorecards fed by:

• wallet inflows/outflows and stability
• merchant payment frequency
• bill payment regularity
• device consistency and SIM tenure (where permitted)

Then move to ML models once you have enough clean repayment outcomes. The win isn’t “more loans.” It’s fewer bad loans at the same approval speed.

Paymenow (South Africa): earned wage access is a trust product

Paymenow ranked #6 overall with 237.8% annual growth, from US$140,000 to US$4.86 million. It offers earned wage access (EWA), positioning itself as a safer alternative to payday loans, with 500,000+ active users across 300+ clients.

The AI lesson: EWA scales on employer integration + user education, and AI can automate both.

Cameroon application: payroll-linked products are emerging across Africa, but adoption dies when onboarding is hard. Use AI tools to:

• automate HR/payroll data mapping during implementation
• run multilingual customer support (French/English + localized phrasing)
• deliver personalized “next best action” nudges (save, repay, budget)

AI isn’t just about risk here. It’s about reducing support tickets and increasing retention.

Inkomoko (Rwanda): growth can come from enabling businesses

Inkomoko ranked #8 overall with 211.2% annual growth, from US$160,000 to US$3.99 million. It supports entrepreneurs in displacement-affected communities with investment, market linkages, and ecosystem partnerships.

The AI lesson: fintech growth doesn’t always mean “more transactions.” Sometimes it means better outcomes for SMEs, which drives revenue via services, partnerships, and financing readiness.

Cameroon application: for telcos and fintechs targeting merchants, AI can power:

• automated cashflow categorization (sales vs stock vs personal spend)
• credit readiness scoring for SMEs
• personalized business tips that actually match the merchant’s reality

This is where “AI content creation” becomes practical: generate merchant-facing insights and SMS/WhatsApp tips that are tied to their actual transaction history.

Chari (Morocco): B2B retail platforms win by predicting demand

Chari ranked #14 overall with 164.4% annual growth, from US$4.04 million to US$74.7 million (2020–2023). It’s an e-commerce + fintech app for traditional retailers, also providing microloans.

The AI lesson: once you own ordering data, you can predict inventory demand and reduce stockouts—then lending becomes safer because you understand the business cycle.

Cameroon application: if you serve distributors, agents, or merchants, use AI to:

• forecast float needs per agent location
• optimize cash/float rebalancing routes
• pre-approve working capital based on reorder patterns

This is a telco-fintech sweet spot: networks provide footprint; AI turns footprint into operational efficiency.

Moniepoint (Nigeria): the POS ecosystem is an AI data engine

Moniepoint ranked #16 overall with 160.3% annual growth, with revenue moving from US$15 million to US$264.51 million (2020–2023). It processes 1+ billion transactions monthly, with US$22+ billion in payment volume, serving 10 million businesses and individuals.

The AI lesson: high-volume merchant payments generate behavioral data that improves:

• fraud detection
• merchant segmentation
• credit decisions
• customer lifetime value modeling

Cameroon application: if you’re pushing QR, POS, or merchant acquiring, treat your data like a product. Build an AI pipeline that answers:

• Which merchants are growing fastest (and why)?
• Which agents/merchants are risky today (not last month)?
• What offer increases usage: lower fees, float credit, or bundled connectivity?

Numida (Uganda): underwriting in minutes requires automation discipline

Numida ranked #17 overall with 151.2% annual growth, from US$230,000 to US$3.57 million. It offers unsecured working-capital loans to MSMEs, with fast digital applications and disbursement to mobile money.

The AI lesson: speed is a feature, but only if defaults don’t explode. That demands a tight feedback loop: underwriting → repayment outcomes → model updates.

Cameroon application: build your lending AI like a “closed loop” system:

1. capture consistent application data
2. track repayment outcomes cleanly
3. retrain models on schedule (monthly/quarterly)
4. include a human review lane for edge cases

If you can’t measure outcomes, you don’t have AI—you have guesswork.

What telcos in Cameroon can copy (without copying the business model)

Telcos already have what many fintechs spend years building: distribution, identity signals, and daily engagement. The mistake is using that advantage only for marketing blasts.

Here are four AI use cases that fit telecoms in Cameroon right now.

1) AI customer service that reduces cost per ticket

Answer first: AI customer support works when it’s trained on your policies and connected to your systems.

A telecom-grade approach includes:

• an AI agent for common issues (SIM swap, PIN reset guidance, fee explanations)
• escalation rules (high-risk fraud topics go to humans)
• multilingual handling (French/English; optionally localized phrasing)

Done well, you cut response time and increase first-contact resolution. Done poorly, you create “polite confusion.”

2) Personalized offers that don’t feel spammy

Answer first: personalization increases conversion because it matches timing and context.

Use AI to decide:

• who should get a wallet promo vs a data bundle
• which merchants need float credit before weekends/market days
• who is likely to churn in the next 30 days

The rule: personalization should reduce messages, not increase them.

3) Network + payment fraud detection as one risk fabric

Answer first: fraud isn’t a payments problem or a telco problem—it’s the same adversary moving across channels.

AI can spot:

• coordinated SIM swaps preceding cash-outs
• abnormal agent settlement behavior
• device farms and synthetic accounts

This is where telecom and fintech collaboration becomes a competitive advantage in Cameroon.

4) Smarter agent network operations

Answer first: agent networks scale when liquidity and trust are managed daily.

AI can help forecast:

• which agents will run out of float
• where new agents should be placed based on demand density
• which agents are underperforming due to location vs behavior

Operational AI is unglamorous. It’s also where margins are protected.

A practical AI rollout plan for Cameroonian fintech teams (60 days)

If you’re trying to generate leads or prove ROI internally, you need wins that show up in a dashboard quickly.

Week 1–2: Pick one metric that matters

Choose one primary metric, such as:

• fraud loss rate (per 10,000 transactions)
• customer support backlog
• loan approval time and default rate
• agent liquidity incidents

If you can’t measure it weekly, don’t automate it yet.

Week 3–6: Build a “human + AI” workflow

Start with a hybrid design:

• AI triages and drafts responses/decisions
• humans approve edge cases
• feedback is captured every time

This avoids the common failure mode: fully automated systems trained on messy data.

Week 7–8: Turn learnings into repeatable playbooks

Document:

• what the AI system does and doesn’t do
• escalation paths
• monitoring thresholds
• data quality checks

That documentation becomes a growth asset when you expand to new regions or partner with a telco.

Snippet-worthy truth: If your AI can’t explain itself to operations, it won’t survive contact with customers.

The lead-generation angle: what buyers in Cameroon are asking for right now

Across telecoms and fintech, I’ve found the same buying questions keep repeating:

• “Can you reduce our fraud losses without blocking good customers?”
• “Can you answer customers faster in French and English without doubling headcount?”
• “Can you personalize offers without spamming users?”
• “Can you help us approve loans faster while keeping defaults stable?”

If your product or service speaks directly to one of these, you’re not selling “AI.” You’re selling a measurable outcome.

Fintech’s dominance in Africa’s growth rankings isn’t a prediction—it’s proof of what happens when distribution meets automation. Cameroon is positioned to ride that same wave, especially where telco rails and AI-driven decisioning reinforce each other.

If you’re building in this space, what would move the needle fastest for you in Q1 2026: fraud reduction, support automation, or personalized growth marketing?