87.5% of Nigerian Fintechs Now Use AI to Fight Fraud – CBN Survey 

A fintech ecosystem survey referenced in the central bank’s reporting shows that 87.5% of Nigerian fintech companies now use artificial intelligence for fraud detection. 

That makes fraud detection the most common AI use case in the sector, well ahead of other popular applications like customer service automation or credit scoring.

The same findings show that 62.5% of fintechs use AI chatbots for customer service. Another 37.5% use AI for credit scoring and risk modelling, while 37.5% also use AI for onboarding and KYC checks. Only 12.5% of respondents said they do not use AI at all. 

Why fraud detection is winning the AI race

Fraud is not a side problem in digital finance. It is one of the biggest daily threats to payment platforms, wallets, agency networks, and digital lending products. 

As transaction volumes grow, criminals also scale. Manual review becomes too slow, too expensive, and too inconsistent when millions of transfers and sign ups are moving through systems every day.

That is why fraud detection sits at the top. It is the most urgent place where AI can deliver fast value by spotting unusual behaviour, flagging suspicious flows, and detecting patterns that humans will likely miss. 

For many fintechs, this is not “nice to have innovation.” It is survival. When fraud losses rise, customer trust drops, chargebacks grow, regulators pay closer attention, and partnerships become harder to keep.

Growth is also raising the compliance burden

The same report also highlights a less celebrated side of scale: compliance and regulatory pressure. A strong majority of operators, 87.5%, said regulatory and risk compliance costs significantly affect their ability to innovate. 

Another 62.5% said regulatory timelines delay product launches. More than one third of respondents said it can take over 12 months to launch new products because of approvals and compliance bottlenecks.

This matters because it explains why AI is being adopted so quickly in risk related areas. The faster the market grows, the more compliance tasks expand. Monitoring, reporting, identity checks, fraud screening, dispute handling, and audit trails all become heavier. 

AI becomes a tool not just for speed, but for keeping up with the compliance load without breaking the business.

Defensive innovation is now a major theme

A lot of people talk about AI adoption as a cool trend. But in payments and lending, AI is often deployed because risk is rising faster than teams can hire and train. 

Fraud detection becomes the first priority because it protects the platform, protects customers, and protects the business licence. The same logic explains why onboarding and KYC use cases are also growing, and why credit scoring appears, even if it is not yet as widespread as fraud controls.

Fintechs also want rules for responsible AI

Operators are not only deploying AI. Many of them also want clearer oversight. About 62.5% said they are very interested in joining an AI focused regulatory sandbox. 

That is a strong signal that fintechs want a structured environment where AI use cases can be tested, reviewed, and guided, instead of leaving companies to guess what will be accepted later.

The survey indicates that 75% of respondents prioritised ethical and transparent AI use in credit and risk decisions. 

That emphasis is important because AI used for risk can impact real lives, including who gets approved, who gets declined, who gets flagged, and who gets locked out.

What customers and SMEs could feel on the ground

If AI use is handled well, everyday users and small businesses could see practical improvements. Fraud detection should reduce account takeovers and social engineering losses, especially when platforms combine AI alerts with better customer support and faster response time. Disputes could also be resolved faster if systems can quickly trace suspicious activity and provide clearer evidence for investigation.

Onboarding could become more consistent too. When KYC checks are automated and properly governed, fewer people should face random delays or unexplained rejections. For SMEs, that can mean less downtime, fewer freezes, and smoother access to financial services.

Fraud detection can be aggressive, and if it is not balanced with strong customer support and clear appeal processes, it can punish innocent users. The goal is safer platforms without turning risk controls into a new source of frustration.