Fraud in Nigerian fintech and financial services continues to evolve as digital transactions scale across payments, lending, and banking platforms. Modern fraud detection requires more than static rules, it depends on recognizing behavioral patterns, monitoring transactions in real time, and understanding how fraud networks operate.
Based on insights shared by Archer, three recurring fraud patterns frequently emerge across fintechs and banks. These patterns reflect broader trends in financial crime across Africa and globally.
This article explains three fraud patterns identified by Archer, why they matter in Nigeria, and how data-driven detection approaches help uncover them.
Stealing from Fintechs and Banks: Transaction splitting and mule networks
One of the most common fraud patterns archer identifies involves criminals stealing directly from fintechs and banks by disguising the size, frequency, or destination of fraudulent transactions.
Key tactics include:
Transaction splitting, where large unauthorized transfers are broken into many smaller transactions to evade amount-based thresholds
Mule accounts, which are legitimate, compromised, or synthetic accounts used to move stolen funds and obscure the origin of fraud
These techniques exploit limitations in traditional rules-based systems that analyze transactions in isolation rather than as part of a broader behavioral or network pattern.
Why this matters in Nigeria
Nigeria’s high transaction volumes, instant payments, and growing digital lending ecosystem make transaction velocity and scale attractive targets for fraud rings. Without behavioral and network analysis, these activities can appear legitimate.
Data-driven detection models analyze timing, frequency, account relationships, and transaction flows to uncover patterns that simple rules miss.
Stealing from customers: social engineering and phishing scams
Another major fraud pattern highlighted by archer focuses on criminals stealing directly from customers rather than from platforms.
Common techniques include:
Social engineering scams, where attackers manipulate users into authorizing transfers or revealing credentials
Phishing attacks, often delivered via email, SMS, or social platforms, impersonating trusted institutions
These scams are especially damaging because transactions are often technically authorized by the user, making detection more complex.
Detection insight
Modern fraud analytics rely on behavioral baselines — how users normally log in, transact, and interact with devices. When behavior suddenly deviates, risk signals emerge even if the transaction amount appears normal.
Real-time monitoring and anomaly scoring allow institutions to intervene before funds permanently leave the system.
Laundering money: rapid movement and network obfuscation
The third pattern archer identifies is relative to money laundering, where illicit funds are rapidly moved through multiple accounts to hide their origin.
This pattern typically involves:
- Fast, repeated fund movement across accounts
- Complex transaction paths designed to confuse audit trails
- Overlap with scam proceeds or stolen funds
Money laundering relies on speed and network complexity to overwhelm traditional monitoring systems.
How analytics uncover this pattern
Graph-based network analysis and temporal transaction monitoring reveal unnatural fund flows, circular movements, and suspicious account relationships that indicate laundering behavior.
By mapping transaction networks instead of analyzing single events, fraud teams gain visibility into coordinated activity.
How data analytics and machine learning detect these patterns
Modern fraud detection combines multiple analytical approaches, including:
- Real-time transaction monitoring to stop fraud as it happens
- Behavioral analytics to identify abnormal user activity
- Machine learning models to adapt to evolving fraud tactics
- Network analysis to uncover linked accounts and coordinated behavior
Together, these techniques significantly improve detection accuracy while reducing false positives, a critical requirement for fast-growing Nigerian fintech platforms.
