Digital payment fraud losses fell 51% to ₦25.85 billion in 2025, down from ₦52.26 billion in 2024, according to NIBSS. Stronger controls, tighter regulation, and wider adoption of BVN and NIN integration all contributed to that decline. That is meaningful progress, but it does not mean the underlying weaknesses that enable fraud have disappeared.

The picture becomes more complicated when you look beyond the headline numbers. While total fraud incidents declined, the amount lost per successful attack increased at several major institutions. What this shows is that some of the same weaknesses continue to surface even as overall losses fall, raising important questions about how bank fraud detection in Africa is evolving.

This article examines five fraud detection gaps that continue to affect African banks, why they persist despite ongoing investment in prevention, and what closing them requires in 2026.

Gap 1 — Monitoring That Isn't Continuous

Many banks treat fraud and risk monitoring as a series of checkpoints rather than a continuous process. A customer is screened at onboarding, then reviewed again at fixed intervals. The space between those checkpoints is often where fraud has time to develop and move.

This gap usually shows up in a few ways:

• Fixed-Interval Reviews: If you review customer risk profiles quarterly or annually, significant behaviour changes can go unnoticed between reviews. This means risk may continue developing for weeks or months before your team identifies it.
• Fraud Between Reviews: Fraud does not wait for the next review cycle. Account takeover, mule account activity, and structuring patterns can develop and complete within days or weeks, often before your next scheduled review takes place.
• Outdated Risk Profiles: A customer who appeared low-risk at onboarding may behave very differently over time. If risk assessments happen only at fixed intervals, your team may be working with a profile that no longer reflects what is actually happening. The result is that risky behaviour can continue while the customer is still being treated as low risk.
• Continuous Monitoring: Real-time or near-continuous risk scoring helps you identify behaviour changes as they happen. Instead of waiting for a scheduled review, you can investigate unusual activity when it first begins to deviate from established patterns.

Even where monitoring runs continuously, the underlying detection logic at most banks still has not caught up with how fraud actually operates today.

Gap 2 — Rule-Based Systems Falling Behind

Most fraud detection at African banks still relies on static, rule-based thresholds. A transaction above a certain amount triggers an alert, while a specific pattern triggers another. The challenge is that fraud has moved well beyond what those rules were designed to catch.

The limitations become clearer when you look at how fraud operates today:

• AI-Generated Documents Pass Checks Built for Manual Forgery: Many document verification rules were designed to detect obvious signs of forgery. They are less effective against high-quality AI-generated documents that can pass visual review without raising immediate concerns. This means you may not identify higher-risk applications until much later in the customer journey.
• Deepfakes Defeat Older Liveness Checks: Many liveness controls were built to detect printed photos or simple video replays. Fraudsters now use more advanced techniques that can create a convincing appearance of legitimacy during identity verification. As a result, your existing controls may struggle to detect newer forms of impersonation.
• Coordinated Mule Networks Stay Below Static Thresholds: Fraudsters can spread activity across multiple accounts, keeping individual transactions below the level that would trigger a rule-based alert. This means significant volumes can move through your systems without generating the signals your investigators expect to see.

The result is that your fraud controls may continue to catch familiar patterns while newer threats pass through unnoticed.

Gap 3 — Disconnected Identity and Transaction Data

At many banks, fraud and compliance teams still work from separate systems. Your fraud team may see suspicious transactions without the identity context needed to assess the risk, while your compliance team holds customer risk information that never connects to transaction activity. The result is that neither team sees the full picture when it matters most.

This gap becomes clearer when you look at how fraud investigations unfold:

• Fraud Can Move Further Before Anyone Sees the Full Picture: A widely reported Nigerian case highlights the challenge. A staff member allegedly exploited a routine transaction reversal function, and the funds moved through 34 intermediary accounts before dispersing into 1,190 secondary accounts across multiple institutions before the pattern was identified. What this shows is that fraud can move a long way before investigators see the full picture when identity and transaction data remain disconnected.
• Transaction Alerts Lack Critical Context: A transaction alert rarely tells the whole story. Your investigators may need to pull identity records, onboarding history, and risk ratings from separate systems before they can assess the case. The result is slower investigations and longer response times.
• Identity Risk Remains Static: A customer's risk profile should evolve as behaviour changes. When identity systems never see transaction activity, risk ratings can remain unchanged long after new risks emerge. This means your team may rely on information that no longer reflects current behaviour.

Even when your systems are connected, too many false alerts can still make genuine risk difficult to find.

Gap 4 — Alert Fatigue From False Positives

High false positive rates remain one of the most persistent challenges in bank fraud detection. The practical effect is that your fraud team cannot give every alert the same level of scrutiny because there simply is not enough time.

The impact shows up across the investigation process:

• Volume Overwhelms Review Capacity: When your fraud team spends most of its time clearing alert queues, there is less time available for detailed investigation. The result is that analysts focus on throughput rather than scrutiny.
• High Noise Reduces Scrutiny: When most alerts turn out to be false positives, analysts naturally begin to expect the next one will be too. This makes it easier for a genuine fraud signal to blend into the noise.
• Poorly Calibrated Thresholds Create Unnecessary Noise: Many false positives stem from thresholds that do not reflect actual customer behaviour. Generic rules often generate far more alerts than controls calibrated to real transaction patterns. The result is more noise for investigators and less attention available for genuinely suspicious activity.

Yet one of the most damaging fraud risks often generates no alert at all: insider fraud.

Gap 5 — Insider Fraud Going Unmonitored

Even as digital payment fraud losses fell sharply in 2025, insider abuse remains one of the biggest fraud risks facing Nigerian banks. NIBSS Managing Director, Premier Oiwoh, stated publicly in early 2026 that insider involvement continues to be the greatest threat, with investigations repeatedly revealing internal participation or controls exploited by staff.

This gap becomes harder to ignore when you look at how insider fraud operates:

• Insider Fraud Can Reach a Significant Scale: In a recent bank fraud case, three employees were arraigned over an alleged ₦8.5 billion fraud involving the manipulation of internal transaction records. This highlights how insider fraud can create losses on a scale that rivals major external attacks.
• Most Controls Focus on Customers: Most fraud detection infrastructure is designed to monitor customer activity. Transaction monitoring, identity verification, and risk scoring typically focus on external behaviour, while staff access to sensitive systems often receives less scrutiny.
• Trusted Access Is the Vulnerability: Insider fraud does not usually begin with a system breach. Employees already have legitimate access to systems, processes, and information. The challenge is that many controls were designed to stop unauthorised access, not misuse by authorised users.

Across all five gaps, the common thread is the same: fraud moves continuously, while many detection systems still rely on fixed checkpoints, static rules, and fragmented data. 

What Closing These Gaps Actually Requires

Closing these gaps does not mean spending more on the same infrastructure. Nigerian banks invested heavily in technology in 2025, yet many of these gaps persist. The challenge is not how much technology you have, but what it is built to do.

The shift requires a different approach:

1. Real-Time Risk Scoring Instead of Fixed Reviews

Risk does not wait for the next review cycle. Continuous risk scoring helps you identify suspicious behaviour as it emerges, reducing the blind spots that fixed review cycles leave behind.

2. Detection That Connects Identity and Behaviour

Identity data and transaction activity should not sit in separate systems. When both operate from shared data, every alert carries the context needed to investigate it properly and make faster risk decisions.

3. Infrastructure Built for African Transaction Patterns

Effective bank fraud detection in Africa requires systems that reflect local identity infrastructure, payment rails, and customer behaviour. Systems designed around these realities can reduce false positives and improve visibility into genuine risk.

As bank fraud prevention in 2026 continues to evolve, the institutions that close these gaps will be the ones that move beyond fixed checkpoints and disconnected systems toward continuous risk detection.

How Dojah's Profiled Risk Helps Close These Gaps

The fraud detection gaps discussed throughout this article have a common theme: risk often develops faster than traditional detection systems can respond. Profiled Risk is built to help institutions move beyond periodic reviews and disconnected data by bringing identity, behavioural, and transaction risk into a single view.

Here's how it addresses the challenges discussed above:

• Continuous Risk Scoring Instead of Fixed-Interval Checks: Risk profiles update as customer behaviour changes rather than remaining static between scheduled reviews. This helps you identify emerging risks sooner and reduce the blind spots created by fixed review cycles.
• Identity and Transaction Data Connected in One Platform: Every alert includes the customer's verified identity, risk profile, and transaction history in one place. The result is less time spent gathering information and more time spent investigating what matters.
• Built on African Identity and Transaction Data: Profiled Risk is designed around African identity infrastructure and transaction patterns. This helps reduce unnecessary alerts while improving visibility into genuine risk.
• Behavioural Monitoring Across the Customer Lifecycle: Risk assessment does not end after onboarding. Behavioural monitoring continues throughout the customer relationship, helping you spot changes that may indicate emerging fraud or compliance concerns.

Profiled Risk brings identity, behaviour, and transaction risk together in one place, helping institutions close the gaps that fragmented and fixed-interval approaches often leave behind.

If you're looking for a fraud detection tool that closes the gaps, see how Profiled Risk brings identity, behaviour, and transaction risk together in one place

Frequently Asked Questions on Gaps in African Bank Fraud Detection

1. Why do fraud detection gaps persist even when banks invest heavily in technology?

Technology alone does not close fraud detection gaps. Many African banks still rely on fixed reviews, disconnected systems, and static rules that struggle to keep pace with how fraud operates today. The challenge is often how technology is used, not how much is spent on it.

2. What are the biggest bank fraud detection challenges in Africa today?

Some of the most common challenges include fixed-interval monitoring, disconnected identity and transaction data, high false positive rates, and insider fraud. These issues continue to affect bank fraud detection in Africa even as institutions strengthen their fraud controls.

3. What does effective bank fraud prevention in 2026 require?

Effective bank fraud prevention in 2026 requires continuous risk monitoring, connected identity and transaction data, and detection systems that reflect local transaction patterns. This helps institutions identify risk earlier and reduce the blind spots that fraud can exploit.