BRD’s New CEO and the Next Wave of Mobile Payments

BRD’s appointment of Stella Rusine Nteziryayo as Chief Executive Officer (pending regulatory approval) isn’t just a leadership headline. It’s a signal moment for anyone building, funding, or scaling fintech and mobile payment services in Rwanda.

Here’s why I care about this change: development banks set the tempo. When they prioritize certain sectors—through credit lines, guarantees, technical assistance, and partnerships—entire markets start moving in that direction. In Rwanda, where day-to-day commerce already leans heavily on phones, the fastest way to widen access and improve trust in digital finance is to back the right rails: interoperable payments, smarter fraud controls, better merchant tools, and AI-assisted operations.

This post sits inside our series “Uko AI Ihindura Urwego rwa Fintech n’Ubwishyu Bukoresheje Telefoni mu Rwanda”—a practical look at how AI is being used across fintech: from customer support and marketing to risk, compliance, and product design. The leadership change at BRD gives us a timely lens to talk about what should happen next.

What BRD leadership changes can unlock for fintech

A new CEO doesn’t change systems overnight, but it can change priorities, speed, and risk appetite. BRD sits in a unique position: it can fund innovation directly, de-risk private capital, and influence how financial institutions collaborate.

If BRD chooses to push harder on fintech and mobile financial services, the impact usually shows up in three places:

1. Capital gets cheaper or easier to access for targeted innovations (merchant networks, agent networks, rural access, SME tools).
2. Partnerships become more intentional—banks, telcos, fintechs, and government programs align around shared infrastructure.
3. Standards improve (data, reporting, governance), which makes it easier for new entrants to plug into the ecosystem.

A blunt truth: most fintech growth stalls not because the app isn’t good, but because distribution, trust, and unit economics break down at scale. Development finance can help fix those weak points—especially when paired with AI that reduces operating cost per customer.

The simplest KPI that matters: cost-to-serve

If you’re running a mobile payments or lending product, you’re living inside one equation:

Lower the cost-to-serve, or you’ll be forced to raise fees—or stop growing.

AI, used responsibly, is one of the few tools that can reduce cost-to-serve without reducing service quality. BRD’s leadership direction can nudge the market toward operational modernization that makes that possible.

Why mobile money and phone-based payments still have “unfinished work”

Rwanda is often recognized as a strong digital policy environment, and mobile money is widely used. Yet even with strong adoption, there’s plenty of unfinished work that affects real people and real businesses.

The pain points I see repeated across founders, merchants, and financial institutions:

• Fraud and social engineering keep evolving faster than manual controls.
• Merchant acceptance is uneven; small merchants still struggle with reconciliation and cash flow planning.
• Interoperability and user experience can feel fragmented across providers.
• Credit access for SMEs remains constrained by thin files and inconsistent transaction histories.
• Customer support bottlenecks create trust issues when disputes take too long.

This matters because mobile payments aren’t just about sending money. They’re the data foundation for savings, credit, insurance, tax compliance, and broader participation in the formal economy.

The December reality: peak season stress-tests the system

Late December is when payment systems get tested: higher transaction volumes, more new users, more scams, more merchant rush. That makes this leadership transition feel especially timely. If Rwanda’s ecosystem wants to enter 2026 stronger, resilience and trust need to be treated like product features, not back-office tasks.

Where AI actually helps (and where it doesn’t)

AI in fintech gets overhyped when it’s talked about like magic. The real value is narrower and more practical: AI automates decisions and conversations at scale, as long as you’ve got clear rules, good data, and human oversight.

Here are high-impact areas where I’d expect BRD-backed institutions and fintech partners to invest.

AI for fraud detection and transaction monitoring

Fraud teams can’t manually review everything—especially during peak seasons. AI models can flag unusual behavior in real time, prioritizing cases for human review.

Practical examples that work in mobile finance:

• Detecting abnormal transaction velocity (many transfers in a short window)
• Identifying SIM-swap risk patterns and account takeover signals
• Scoring merchant risk based on transaction anomalies
• Reducing false positives so legitimate users aren’t blocked

The win isn’t “perfect fraud prevention.” The win is faster detection + fewer innocent users penalized, which improves trust.

AI for credit scoring using alternative data—done responsibly

Many SMEs and individuals have limited traditional credit history. Mobile payment trails can help, but only with clear consent, governance, and fairness testing.

A responsible alternative-data credit approach typically includes:

• Transparent consent language (what data is used and why)
• Bias testing (to avoid excluding certain groups)
• Human appeal processes (customers can contest decisions)
• Conservative rollout (start small, monitor outcomes)

If BRD prioritizes this area, it can catalyze more SME lending by financing pilots and setting governance expectations.

AI in customer support and dispute resolution

This is the “unsexy” area that often delivers the fastest ROI. AI-assisted support—think smart routing, draft replies, multilingual help, intent detection—can cut response times and reduce churn.

For Rwanda’s context, bilingual and multilingual support (Kinyarwanda, English, French) is a practical advantage. AI can help support agents respond faster and more consistently, but it shouldn’t be used to dodge accountability. Customers still need an easy path to a human.

AI for operations: reconciliation, reporting, and compliance

A lot of fintech teams quietly spend massive time reconciling transactions, cleaning spreadsheets, and preparing compliance reports. AI can help categorize transactions, detect mismatches, and draft reports.

That reduces operational risk and frees teams to build better products. If BRD pushes for stronger reporting standards while funding modernization, the ecosystem becomes more investable.

What a “fintech-forward” BRD agenda could look like in 2026

If BRD’s new leadership wants to strengthen Rwanda’s mobile financial services, there’s a clear playbook. It’s not about chasing flashy tech. It’s about funding infrastructure, de-risking innovation, and enforcing healthy discipline.

1) Targeted financing for payment infrastructure and merchant tooling

The biggest adoption accelerators often live with merchants:

• Affordable acceptance tools
• Faster settlement options
• Simple reconciliation dashboards
• Working-capital products linked to transaction flows

Funding these areas improves the entire payments loop, not just consumer wallets.

2) Ecosystem partnerships that reduce fragmentation

Fintech ecosystems grow when stakeholders coordinate:

• Banks: compliance maturity and balance sheets
• Telcos: distribution and agent networks
• Fintechs: product velocity and customer obsession
• Public sector: identity, standards, and inclusion priorities

BRD can be the connector that turns “we should collaborate” into funded, measured programs.

3) AI governance as a requirement, not an afterthought

If AI is going to power credit decisions, fraud controls, or customer communications, governance must be baked in. That includes:

• Model monitoring (drift, performance, fairness)
• Audit trails (why a decision happened)
• Data minimization (collect only what’s needed)
• Incident response plans (what happens when something goes wrong)

The market doesn’t need more AI demos. It needs trustworthy AI in production.

4) Practical capacity-building for fintech operators

One underrated move is funding training and shared services: compliance playbooks, security baselines, and data governance templates. Many early-stage fintechs fail not because they lack ambition, but because they hit regulatory and operational complexity too late.

If you run a fintech or mobile payments business: what to do next

Leadership changes at influential institutions create windows of opportunity. If you’re building in Rwanda’s fintech space, here are moves that tend to pay off.

Build a BRD-ready story: impact + unit economics

Development finance cares about impact, but it also cares about sustainability. Your pitch should answer:

• What problem are you solving (and for whom)?
• How does your model reduce cost-to-serve over time?
• What are your fraud and compliance controls?
• What metrics prove adoption (active users, retention, merchant repeat usage)?

A strong stance: don’t sell “AI” as the product. Sell the measurable outcome AI enables—lower fraud losses, faster dispute resolution, higher merchant retention.

Strengthen your data foundations before you “add AI”

AI doesn’t fix messy data; it amplifies it. Prioritize:

• Clean customer and transaction identifiers
• Consistent event tracking (what users do in-app)
• Clear data retention policies
• Role-based access controls

Then add AI where it reduces workload or risk.

People Also Ask: “Will AI replace agents and support staff?”

AI won’t remove the need for humans in mobile finance; it changes the job.

• Agents still matter for cash-in/cash-out and trust building.
• Support staff still matter for edge cases, escalations, and empathy.
• The winning teams use AI to handle repetitive work and free humans for the hard problems.

The business result is better service at scale—not a race to eliminate humans.

What Stella Nteziryayo’s appointment could mean—if the ecosystem pushes

The appointment of Stella Rusine Nteziryayo at BRD arrives at a moment when Rwanda’s digital finance ecosystem is ready for the next layer: more resilience, more interoperability, more SME-focused products, and stronger governance around AI-driven decisions.

For our series on AI in fintech and phone-based payments, this is the bigger point: technology adoption isn’t just about tools. It’s about leadership choices—what gets funded, what gets measured, and what standards become normal.

If you’re a fintech founder, bank product lead, or payments operator, now is the time to sharpen your roadmap for 2026: focus on trust (fraud + disputes), merchant value (reconciliation + working capital), and responsible AI (governance + monitoring). Those priorities win customers, and they win partners.

So here’s the forward-looking question I’ll leave you with: if BRD decides to push hard on AI-enabled mobile financial services in 2026, are you positioned to be a credible partner—or will you look like a risky bet?