How we approach AI Governance at Paystack

Artificial Intelligence (AI) is changing how financial products get built.

In fintech, teams use AI to spot suspicious transactions sooner, review code faster, summarize large volumes of data, and automate everyday tasks. When it works well, AI helps systems run more smoothly and gives businesses more time to focus on what matters most.

But fintech runs on trust. These systems handle sensitive data and move real money. When AI goes wrong, the impact can be serious. Data may be exposed, decisions can become hard to explain, and confidence in the business can be lost.

At Paystack, we believe AI should make financial systems safer, not riskier, and more dependable, not harder to trust. That belief shapes how we use AI today and how we plan to build with it in the future.

This post explains how we think about AI governance, why it matters in fintech, and what it means for the businesses that rely on us.

AI is evolving fast, and the stakes are rising

AI adoption across fintech is accelerating. Inside companies, teams are using AI to analyze transactions, respond to customer questions, review code, and automate internal workflows.

For African fintechs, the upside is meaningful. AI can help detect fraud earlier, support teams operating across multiple markets, and reduce friction for merchants and their customers. When used well, it can make financial systems faster and more reliable.

But the risks are just as real. As AI use has increased, reported AI-related incidents have risen sharply too. In fintech, a poorly governed AI system can do real damage. For example, an AI tool trained on transaction or support data without proper controls could expose sensitive customer information, or make automated decisions that teams can’t clearly explain or stand behind. In regulated environments, that lack of clarity can quickly become a compliance issue, not just a technical one.

Regulators are starting to respond, with new AI-related rules emerging globally and across Africa. In Nigeria, for example, a proposed National Digital Economy and E-Governance Bill is expected to be passed by March 2026. The bill aims to give regulators new levers of control over algorithms, data, and digital platforms.

Still, trust doesn’t come from regulation alone. It comes from building systems that behave predictably, protect data, and hold up under scrutiny.

That’s why how AI is used matters as much as whether it’s used at all. In a regulated industry like fintech, weak AI governance can limit what’s possible, put merchants at risk, and compromise trust.

At Paystack, responsibility is part of how we think about building from the very beginning.

How we use AI at Paystack

Like many fintech companies, Paystack relies primarily on third-party AI tools to support everyday work, from fraud analysis and automation to code generation and internal data exploration. For example, we use Gemini for day-to-day productivity, while tools like Cursor, Copilot, and Claude Code support agentic coding and contextual assistance throughout the software development lifecycle.

These tools can be powerful. But their value doesn’t come from performance alone. It comes from how they’re governed.

When a team at Paystack wants to introduce a new AI tool, they must document ownership, purpose, cost, why the data it relies on can be used, the types of data it processes, and the risks it may introduce. Each proposal is reviewed centrally before approval, so every AI system in use has clear accountability and oversight from the start.

To make this work across the company, we rely on a set of governance practices that guide how AI is introduced and used.

• A living inventory of AI tools: We maintain a central inventory of AI systems in use, including what they're used for, the data they interact with, and the level of risk involved. This gives us visibility across teams and helps prevent silent or duplicated use of tools that haven't been properly reviewed.
• Careful evaluation of third-party tools: Before adopting any AI tool, teams are required to document how it works, what data it collects, whether that data is used for model training, and what security controls are in place. Tools that don't meet our privacy or security requirements aren't approved, even if they're popular or widely used elsewhere.
• Extra scrutiny for high-impact use cases: Some areas carry more risk than others. Free AI tools are prohibited for use in regulated industries. Use cases like fraud detection, automation, and agentic coding receive deeper review because they can affect customers, merchants, or the reliability of our systems. In these cases, we look closely at explainability, human oversight, and failure modes.
• Clear expectations for how teams use AI: We invest in training and shared awareness through internal sessions, simulations, and open discussions. The goal isn't just to help teams use AI effectively, but to help them understand its limits. AI tools are treated as assistants, not decision-makers, and teams remain responsible for validating outputs.
• Ongoing review, not one-time approval: AI use isn't approved once and forgotten. We revisit tools and practices as regulations evolve, risks change, or new use cases emerge. In some cases, this leads to tightening controls or retiring tools altogether.

This approach means we sometimes move more slowly than we could. That tradeoff is intentional. Using AI responsibly means being willing to pause, ask hard questions, and say no when the risks outweigh the benefits.

To make sure this approach holds up beyond our own judgment, we ground it in established global and regional standards.

Aligning with global standards

One of the main frameworks we draw from is ISO/IEC 42001. It’s the first international standard focused specifically on how organizations manage AI systems. At a practical level, it helps answer questions like: What data is this system using? What risks does it introduce? Who is responsible for monitoring it? And how should it be reviewed as things change?

We also track regulatory frameworks like the EU AI Act, which groups AI systems by risk and sets clear expectations for oversight, transparency, and human involvement. While it’s a European regulation, it’s shaping how companies around the world think about acceptable AI use, especially in high-impact industries like finance.

At the same time, we pay close attention to how AI regulation is evolving across African markets. Local context matters. Data protection rules, consent requirements, and cross-border realities aren’t the same everywhere. Aligning with both global standards and regional expectations helps us build systems that work responsibly across the markets we serve.

These standards give us a shared language for thinking about AI risk. Our governance principles translate that guidance into everyday decisions.

The principles behind our AI governance framework

The way we evaluate tools, manage risk, and align with standards is guided by a small set of principles. These principles turn AI governance from a checklist into a way of making everyday decisions.

They help teams answer practical questions: Is this the right tool? Is this the right use case? And where should we draw the line?

• Start with the data: We pay close attention to the data AI systems rely on. That means identifying sensitive information, watching for bias, and making sure datasets are appropriate for the job at hand. If data isn't sound, the system isn't either.
• Keep humans in the loop: AI supports people, it doesn't replace them. For decisions that affect customers, merchants, or core systems, there's always meaningful human review. Teams remain accountable for outcomes, even when AI is involved.
• Built privacy in from the beginning: Consent and privacy aren't added later. Safeguards around data access, use, and retention are designed into systems from the start, especially where personal or sensitive information is involved.
• Match controls to risk: Not all AI use cases carry the same level of risk. Higher-impact areas receive deeper scrutiny, stronger controls, and clearer limits. Lower-risk uses are handled differently, without slowing teams down unnecessarily.
• Hold vendors to the same standard: When we rely on third-party AI tools, responsibility doesn't stop at onboarding. Vendors are reviewed continuously to ensure they still meet our security, privacy, and compliance expectations as their products evolve.
• Expect change: AI technology and regulation evolve quickly. Our framework is designed to evolve with them, so governance stays relevant as new tools, use cases, and risks emerge.

Responsible AI at Paystack isn’t owned by one team. It’s a shared responsibility across engineering, product, compliance, security, and operations, built into how we work together every day.

What this means for merchants and their customers

All of this work ultimately exists to support the businesses that rely on Paystack every day.

Strong AI governance helps us build systems that behave predictably and protect sensitive data. For merchants, that shows up in a few important ways:

• More dependable fraud detection, with fewer false positives and clearer decision paths
• Secure, seamless payment experiences that customers can trust
• Greater confidence in the infrastructure handling critical transactions and sensitive information

Our goal is to use AI carefully, so merchants can focus on running their businesses without worrying about what’s happening behind the scenes.

Looking ahead

Building responsibly with AI isn’t a one-time effort. It’s an ongoing process of learning, testing, and adjusting as tools improve and expectations evolve. We’ll continue to refine our approach and share what we learn along the way.

Over time, we expect to explore more proprietary AI capabilities, but always with safety, compliance, and ethical use as non-negotiables.

At Paystack, we believe Africa’s digital economy can grow through both innovation and care. By approaching AI thoughtfully and deliberately, we aim to support progress that lasts, for merchants, for customers, and for the ecosystem as a whole.