Africa’s Fastest-Growing Fintechs: AI Lessons for Cameroon

Seven of Africa’s top 20 fastest-growing companies are fintechs. That’s not a “nice-to-have” headline—it’s a signal that money is now a software problem, and Africa is solving it quickly.

The Financial Times and Statista ranking (based on revenue CAGR from 2020–2023) shows fintech taking 35% of the top 20. The standouts grew revenues at 151.2% to 583.6% per year—numbers that don’t happen through hustle alone. They happen when customer acquisition, risk checks, support, collections, and compliance move from manual work to automated systems.

This post sits inside our series on how AI is transforming telecommunications and fintech in Cameroon. The point isn’t to copy Nigeria, South Africa, or Morocco. The point is to extract patterns from what’s working across the continent—and map them to Cameroon’s mobile-first reality where telcos, mobile money, agent networks, and fintechs are forced to operate at scale with thin margins.

Fintech is dominating growth because it’s built for volume

Fintech tops these rankings because the unit economics reward companies that can serve millions of small transactions, not a few large ones. When your average customer tops up, pays a bill, or takes a micro-loan, you win by being fast, cheap, and always available.

Across the ranked companies, you can see three volume-driven models repeating:

• Payments + agent networks (e.g., PalmPay, Moniepoint): win by being everywhere.
• Digital lending for MSMEs (e.g., Numida, eShandi): win by deciding risk quickly.
• Embedded finance for merchants (e.g., Chari): win by owning distribution and data.

Cameroon fits this pattern perfectly. You’ve got high mobile usage, a strong preference for mobile money flows, and a market where many businesses are informal or semi-formal. That creates demand—but it also creates operational headaches (KYC, fraud, disputed transactions, and support). The companies growing fastest in Africa aren’t “brave” about these headaches. They systematize them.

The contrarian takeaway for Cameroon

Many teams assume growth comes from adding more agents, more branches, or more salespeople.

Most companies get this wrong.

In payments and lending, growth comes from reducing the cost of serving one more customer. AI is how you do that without hiring endlessly.

What the top fintechs are really doing (and where AI fits)

The source article lists seven fintechs in Africa’s top 20 fastest-growing companies: PalmPay (#2), eShandi (#4), Paymenow (#6), Inkomoko (#8), Chari (#14), Moniepoint (#16), and Numida (#17). Their products differ, but their operating playbook rhymes.

PalmPay and Moniepoint: scale is a data problem

PalmPay reported revenue rising from US$200,000 (2020) to US$63.9M (2023) with 583.6% annual revenue growth, and claims 35M+ users and up to 15M transactions daily. Moniepoint reported revenue moving from US$15M (2020) to US$264.51M (2023) with 160.3% annual growth, processing 1B+ transactions monthly and US$22B+ in payment volume.

At that size, the “product” isn’t just an app. It’s the machinery behind it:

• Fraud detection in real time (velocity checks, anomaly detection, device fingerprinting)
• Automated customer support (ticket triage, self-serve flows, multilingual responses)
• Personalized engagement (next-best action messaging, churn prediction)

In Cameroon, where trust is everything, AI-backed fraud monitoring and faster dispute resolution can be the difference between a customer staying or switching. If your support queue takes three days, your competitor’s chatbot that resolves 40% of issues instantly will look like magic—even if it’s just good automation.

eShandi and Numida: lending wins when underwriting is fast and fair

eShandi posted 276.4% annual revenue growth (2020–2023) and positions itself around last-mile financial services. Numida posted 151.2% annual revenue growth and focuses on unsecured working-capital loans to MSMEs with rapid disbursement.

Digital lending in Africa succeeds when it does two things at once:

1. Approves quickly (minutes, not days)
2. Keeps defaults under control without excluding good borrowers

AI fits here, but not as buzzwords. Practical AI in lending looks like:

• Alternative credit scoring using transaction patterns and business behavior
• Early warning signals for repayment risk
• Smarter collections that prioritize respectful, targeted outreach instead of blanket pressure

For Cameroon’s fintech and mobile money ecosystem, this matters because many customers don’t have formal credit histories. You can still lend responsibly if you model behavior—and if you design transparency into decisions.

Paymenow: employee finance is a retention product

Paymenow grew at 237.8% annually and offers earned wage access (EWA)—employees access part of their wages before payday. That’s a fintech product, but it’s also an HR tool.

Cameroon has large employers in sectors like telecom, logistics, and services. There’s an opening for wage-linked products, especially when paired with:

• automated affordability checks,
• budgeting nudges,
• financial education content delivered in-app.

AI helps by tailoring advice and preventing people from cycling into dependency (for example, by flagging repeated early withdrawals and offering a repayment plan or coaching content).

Chari: distribution beats features

Chari grew revenues to US$74.7M (2023) with 164.4% annual growth, combining commerce for retailers with microloans. The lesson: merchants don’t wake up looking for “fintech.” They wake up needing stock, delivery, and reliable suppliers.

In Cameroon, merchant-focused fintech products are most effective when they sit inside daily workflows:

• merchant payments + inventory insights
• embedded credit at checkout
• automated reconciliation for small shops

AI becomes useful when it turns messy merchant activity into simple prompts: “You’ll run out of cooking oil in 5 days” or “This week’s sales suggest a safe credit limit of X.”

Why telecom matters: Cameroon’s AI-fintech future is telco-adjacent

The reality? It’s simpler than you think: in mobile-first economies, telecom is the distribution layer for fintech.

When telcos provide identity rails, reliable connectivity, agent networks, and mobile money ecosystems, fintechs can focus on products. When those rails are weak, fintechs spend money reinventing basics.

Here’s where AI connects telecom and fintech in Cameroon in very practical ways:

1) Customer onboarding and KYC that doesn’t kill conversion

AI-supported onboarding reduces drop-off while staying compliant:

• ID capture quality checks (blurry photo detection)
• form autofill and validation
• anomaly detection for repeated identities or suspicious devices

If you’re in Cameroon building fintech products, treat onboarding like a revenue engine. Every extra step costs you customers.

2) Fraud prevention across SIM, device, and transaction behavior

Fraud isn’t just “a fintech problem.” It travels through phones, SIM swaps, and social engineering.

AI can connect signals:

• unusual SIM change + new device + high-value transfer = higher risk
• repeated failed PIN attempts = account takeover attempt

Telco-fintech collaboration on risk signals (with privacy safeguards) is one of the highest ROI moves the market can make.

3) Support that actually scales during peak periods

End-of-year spending (like December) pushes transaction volumes and complaint volumes up at the same time. Human-only support breaks first.

A good AI support stack in fintech and telecom includes:

• automated categorization (payments failed, cash-out issue, chargeback, KYC)
• multilingual response templates (French, English, and localized phrasing)
• escalation rules that route high-risk tickets to humans immediately

Customers don’t demand “AI.” They demand fast answers.

A practical AI roadmap for fintech teams in Cameroon

If you’re leading a fintech, telco product team, or a payments business in Cameroon, you don’t need a massive AI lab. You need 90-day wins that reduce cost and increase trust.

Step 1: Pick one metric that hurts and automate around it

Good starting metrics:

• onboarding completion rate
• fraud loss rate per 10,000 transactions
• average time to resolve support tickets
• loan approval time
• repayment rate by cohort

Then apply targeted AI or automation. The goal is measurable change, not “AI adoption.”

Step 2: Start with “human-in-the-loop” designs

In regulated environments, fully automated decisions can backfire.

What works:

• AI suggests, humans approve for edge cases
• AI drafts responses, support agents send
• AI flags suspicious activity, risk team reviews

This keeps accuracy high while building internal confidence.

Step 3: Build trust through explainability and customer language

If a customer is declined for credit, “system decision” is not an explanation.

Simple, trust-building alternatives:

• “We couldn’t verify your business activity from recent transactions.”
• “Your account shows unusual activity; we paused transfers to protect you.”

Clear language reduces complaints and social media damage.

Step 4: Use AI for growth content, but tie it to product behavior

Marketing is where many teams waste money. AI can help, but only if content is driven by real customer needs.

Examples that work in Cameroon:

• automated FAQ articles triggered by top support issues
• personalized in-app tips after failed transactions
• agent-facing scripts that improve first-time success for onboarding

Content is customer support in disguise. Treat it that way.

People also ask: “Does AI mean fewer jobs in fintech and telecom?”

AI usually shifts jobs rather than removing them.

In fast-growing fintech markets, the constraint isn’t “too many staff.” It’s that support agents, risk analysts, and ops teams are overwhelmed. AI absorbs repetitive work so people can handle exceptions, partnerships, and product improvements.

A line I use internally is: AI doesn’t replace teams; it replaces bottlenecks.

What to copy from Africa’s fastest-growing fintechs (and what to avoid)

Cameroon can borrow patterns without importing problems.

Copy these patterns

• Operational excellence: faster support, better fraud controls, cleaner reconciliation
• Distribution-first strategy: agents, merchants, and telco rails are growth channels
• Data discipline: define what “good customer” behavior looks like and model around it

Avoid these traps

• Over-automating credit decisions with no appeals process
• Growing transaction volume without strengthening dispute handling
• Treating AI as a branding exercise instead of a cost-and-trust engine

If your AI doesn’t reduce losses, shorten resolution times, or improve conversion, it’s a hobby—not a strategy.

Where this goes next for Cameroon in 2026

The fintech companies topping Africa’s growth charts are proof that the market rewards scale. Cameroon’s opportunity is to combine that scale mindset with AI-driven operations and telecom-powered distribution.

If you’re building in this space, the next step is simple: audit your customer journey, find the two biggest friction points, and design automation that makes them disappear—without breaking compliance or trust.

If you want a practical plan, we can help you map an AI roadmap across onboarding, support, fraud, and engagement using the tools you already have. What would you fix first: onboarding drop-offs, fraud losses, or support backlogs?