This guide explores how artificial intelligence is transforming fraud detection in payments, with special attention to Nigeria's unique challenges and innovations.
The numbers tell a sobering story. Between early 2023 and mid-2025, Nigeria's financial services industry lost hundreds of billions of naira to digital fraud, while over 281,500 user accounts were exposed through data breaches and leaks in just the first quarter of 2025 alone. This isn't a distant threat—it's a present reality affecting ordinary Nigerians daily.
The scale of the challenge is further underscored by Interpol's 2026 African Cyberthreat Assessment Report, which reveals that AI enables 55% of cybercrimes across Africa, with Nigeria identified among the worst-hit countries alongside South Africa and Namibia [4]. According to the report, cybercriminals are exploiting vulnerabilities in KYC enforcement and SIM card registration processes, using AI tools to bypass verification systems [4].
Account takeover remains one of the most persistent threats, contributing significantly to banking-related fraud losses reported across the sector. Industry data shows that account takeover accounts for a significant share of banking-related losses reported to the Nigeria Inter-Bank Settlement System, with fraudsters becoming more organised and sophisticated.
The Central Bank of Nigeria (CBN) has responded decisively. In June 2026, it unveiled the Payments System Vision 2028 (PSV 2028), setting an aggressive regulatory target: reduce electronic fraud losses and strengthen the country's digital payments ecosystem. This ambition demands more than traditional security measures—it requires a fundamental rethinking of how fraud is detected and prevented.
The CBN's vision relies on AI-driven identity verification, real-time monitoring, and fraud neutralization capabilities. As CBN Governor Olayemi Cardoso framed it: "With NIN, BVN, intelligent systems, and AI fraud detection, people's money must be safer in the digital system than under their mattresses. A payment system is only as strong as the trust people place in it."
The trust imperative cannot be overstated. For Nigeria's digital economy to continue growing, consumers must believe their money is safe in digital channels. That trust is the foundation upon which financial inclusion is built. Cardoso projected that financial inclusion will reach 95% by 2028 under PSV 2028, ensuring that more market women, farmers, entrepreneurs, and young people participate in the formal financial system.
AI systems analyze hundreds of variables per transaction in milliseconds, adapting to new fraud patterns continuously.
For years, fraud detection relied on static, rules-based systems. These systems operate on simple logic: flag transactions exceeding a certain threshold, block payments from suspicious IP addresses, or reject login attempts from unfamiliar devices.
The problem is that fraudsters have evolved far beyond what these rules can catch. Traditional fraud systems in Nigeria have relied heavily on static rules that are slow to change and often generate large volumes of false positives. While such rules remain necessary, risk leaders say they struggle to keep up with evolving fraud tactics that span multiple accounts, devices, and customer journeys.
Academic research confirms this growing inadequacy. A comprehensive study published in the African Banking and Finance Review Journal examining Nigerian banks from 2013 to 2024 found that AI-driven fraud detection has a positive and significant impact on Return on Investment (ROI) at the 10% level (β = 0.222, p = 0.074) [1]. This suggests that "value preservation through automated security is a primary driver of stability" in the Nigerian banking context [1]. The researchers identified what they term a "Productivity Paradox," noting that massive investments in digital volume and human capital have not yet yielded significant marginal returns due to high infrastructure maintenance costs and skill gaps [1].
Rules-based systems have another fatal flaw: they generate enormous false-positive rates. When legitimate users are constantly blocked or challenged, they lose trust in the platform. A hybrid AI model developed for the Nigerian FinTech sector demonstrated superior performance, achieving an F1-score of 0.92 and an AUC-ROC of 0.98 while significantly outperforming benchmark models in accurately flagging fraudulent transactions and minimizing false positives [2].
The slow pace of rule updates compounds the problem. When fraudsters develop a new tactic—say, a phishing campaign targeting specific demographics—traditional systems require manual rule updates. This reactive approach means institutions are always one step behind, patching vulnerabilities after losses have occurred. AI systems, by contrast, learn continuously. They don't need to be told what to look for—they observe patterns, adapt to new behaviors, and flag anomalies that no human rule could have anticipated.
AI-powered fraud detection isn't just an incremental improvement—it represents a fundamental paradigm shift. Unlike static rules, AI systems can analyze thousands of transactions per minute, assigning risk scores that update in real-time.
According to the Central Bank of Nigeria's Fintech Report 2025, published in February 2026, a staggering 87.5% of Nigerian fintech companies now deploy AI for fraud detection. This makes fraud detection the dominant use case, far outpacing other applications such as customer service chatbots at 62.5% and credit scoring at 37.5% [5].
The magic lies in pattern recognition. An AI system might notice that a transaction is unusual not just because of its size or location, but because of its size, location, timing, the device used, and hundreds of other variables that together create a behavioral fingerprint. No static rule can capture this complexity.
Advanced hybrid models are setting new benchmarks for performance. A study proposing a hybrid AI framework integrating Graph Neural Networks (GNNs) and Isolation Forest algorithms for Nigerian FinTechs demonstrated exceptional results: the hybrid approach achieved an F1-score of 0.92 and an AUC-ROC of 0.98, significantly outperforming traditional models like Logistic Regression and Random Forest [2]. The researchers concluded that "a hybrid model is particularly suited for the unique challenges of the Nigerian FinTech landscape" [2].
Similarly, the Moniguard enterprise fraud detection system, optimized for Nigerian e-commerce with specific features for Pay-on-Delivery transactions, achieved 96%+ detection accuracy while maintaining less than 50ms inference latency [9]. The system uses a multi-model ensemble combining XGBoost, Isolation Forest, Autoencoder, and Graph Neural Networks to identify coordinated fraud rings and recycling patterns [9].
Global cognitive AI research further validates the potential of these approaches. A study published in Wiley's academic literature demonstrated a cognitive AI framework for fraud detection that achieved an F1 score of 96.8% and detection latency of just 21 milliseconds per event, significantly outperforming conventional deep learning baselines [3]. The system also reduced false positives by 18.6%, highlighting the robustness of AI-powered approaches in high-frequency environments [3].
Real-time analysis is perhaps the most critical capability. Traditional methods rely on historical data analysis—reviewing what already happened. AI systems continuously analyze data streams as they occur, allowing for immediate identification of anomalies. Imagine a fraudster attempting an unauthorized withdrawal from a bank account. An AI-powered system, constantly monitoring transaction activity, can detect this anomaly in real-time and trigger an alert, potentially preventing the transaction before funds leave the account.
The scale advantage is equally important. Financial institutions generate massive datasets daily. Manually analyzing such quantities is impossible—humans simply cannot process information at machine speed. AI systems effortlessly process these datasets, identifying hidden patterns and connections that would escape human scrutiny.
The CBN noted that despite significant growth in digital payments, the sector remains vulnerable to cyberattacks, identity theft, phishing schemes, unauthorised transactions and other forms of financial fraud that threaten consumer trust and financial inclusion. AI-powered fraud detection is therefore not optional—it is becoming essential to staying ahead of increasingly sophisticated threats.
PalmPay, one of Nigeria's largest digital payments platforms, has built its cybersecurity strategy around an AI-powered security ecosystem. The company integrates multiple layers:
This layered approach reflects a broader industry shift from isolated security controls to connected, intelligence-driven systems.
The company has also deployed a Fraud Case Management System that centralizes fraud intelligence, allowing security teams to detect patterns, investigate incidents more efficiently, and respond faster to emerging threats.
According to Omoruyi Aiyudu, PalmPay's Anti-Fraud Manager: "Fraud prevention today requires a shift from isolated security controls to a connected, intelligence-driven security ecosystem. At PalmPay, we leverage AI-driven fraud detection, behavioural analytics and multi-layered authentication to identify and mitigate risks in real time."
PalmPay has also expanded partnerships with regulatory bodies and security agencies, including collaboration with the Nigerian Cybersecurity Unit of the Nigeria Police Force, where the company has supported fraud detection and investigation initiatives through advanced equipment provision. The company has also worked with the Nigerian Data Protection Commission to enhance internal data governance practices and strengthen compliance with the Nigeria Data Protection Act 2023.
OPay has adopted a differentiated security architecture that applies verification measures only when needed. Rather than subjecting every transaction to the same level of scrutiny, OPay's system automatically triggers facial verification only when high-risk activities are detected—such as high-value transfers, transactions from unfamiliar locations, or unusual patterns.
As Dotun Adekunle, OPay's Chief Operating Officer and Chief Technology Officer, framed it: "Your face is the final lock on risky transactions, invisible when things are normal, immovable when they are not."
OPay's security features demonstrate thoughtful design:
The company has also redesigned its payment infrastructure to enable transaction confirmations in less than one second while maintaining a transaction success rate of 99.9%. This demonstrates that strong security and excellent user experience can coexist.
Oxygen X, the credit-led fintech subsidiary of Access Holdings, has taken a fundamentally different approach: rather than treating fraud detection as an add-on, the company embedded it directly into core infrastructure from the beginning.
The approach has paid off impressively. Over a six-month period, Oxygen X recorded nearly four-fold revenue growth while improving approval rates by about five percent. This challenges the assumption that stronger fraud controls must come at the expense of customer experience.
The company deployed Archer as its core fraud detection and investigation platform, embedding it into backend systems and daily risk workflows. The platform enables real-time monitoring, behavioral clustering, and entity resolution—tools designed to identify patterns that appear benign when viewed account by account but reveal coordinated fraud when examined holistically.
Daniel Watts, Oxygen X's Chief Risk Officer, explained: "Fraud in Nigeria doesn't stand still, and a lot of the most harmful activity looks perfectly normal in isolation. What has mattered for us is being able to surface coordinated behavior early, before it escalates, instead of discovering it after losses have already happened."
The proactive approach has allowed Oxygen X to scale without increasing headcount, as analysts spend less time investigating low-quality alerts and more time focusing on genuine risk. The same infrastructure is now being extended to support anti-money laundering and compliance workflows.
Recent academic research emphasizes the importance of comprehensive deployment pipelines. The MLOps-Fraud-Detection project, designed specifically for Nigerian market conditions, demonstrates how modern MLOps practices can dramatically enhance fraud detection capabilities [9]. The system, called Moniguard, is tailored for Nigerian e-commerce and addresses challenges specific to the local market:
The economic impact is substantial: the Moniguard system prevents ₦4.2 million daily in fraud while achieving a remarkable 77,000% ROI (₦195M monthly savings versus $2,500 monthly cost) [9].
While financial institutions deploy AI for defense, criminals are using it for offense. Generative AI has become a powerful weapon in the fraudster's arsenal, enabling attacks that would have been impossible just a few years ago.
The Pulitzer Center has documented how AI is being weaponized in Nigeria's latest wave of Ponzi scams [8]. Fraudsters now use AI-generated dashboards, deepfake endorsements, trading bots, and voice clones to deceive victims at scale [8]. These scams promise high returns through "automated" trading systems that appear legitimate but are built on illusion.
In 2025, Crypto Bridge Exchange (CBEX) collapsed after luring thousands into activating a so-called "AI trading feature." Victims lost billions of naira overnight after fake trades and smooth interfaces gave way to frozen accounts and vanished funds [8]. CBEX, which falsely claimed affiliation with the China Beijing Equity Exchange, used AI-generated trading logs, 24/7 bot-run customer service, and fake social media credibility to convince investors it was legitimate [8].
Other platforms like PrimeAuroraPlatform have used AI to create interviews with public figures endorsing their schemes, deepening public trust and expanding their reach in Nigeria [8].
Nigerian social media is flooded with deepfake content featuring celebrities and public figures promoting fraudulent investment schemes. Within just one week of researching this topic, a journalist encountered five separate deepfake scams on TikTok featuring Wizkid, Rema, Ngozi Okonjo-Iweala, former President Olusegun Obasanjo, and even President Tinubu.
In February 2026, Ngozi Okonjo-Iweala had to log onto X to deny promoting an investment scheme requiring N380,000 for returns of N2.6 million in one week. A synthetic version of her face and voice had been circulating across WhatsApp, Instagram, and Facebook. Ibukun Awosika, former Chairman of First Bank of Nigeria, spent January doing the same thing, debunking AI-generated videos that placed her endorsement behind investment platforms she had never heard of.
Forensic expert Joshua Olugbenga Fatogun warns that Nigerian banks face a genuine risk of deepfake attacks, especially given the widespread use of WhatsApp communications, voice notes, and remote approvals in business operations [6]. He emphasizes that "fraud is no longer purely human-driven but is becoming machine-assisted and scalable," with a single fraudster equipped with AI tools now able to execute attacks that previously required an organized syndicate [6].
The technology is accessible and cheap. Tools that once required specialist knowledge and expensive hardware are now available freely to anyone with a smartphone. According to Surfshark, deepfake incidents globally surged by 257% in 2024, and the first quarter of 2025 alone recorded more incidents than the entire preceding year.
The victims are not celebrities—they're ordinary Nigerians. The people whose faces are stolen can at least defend themselves. Most Nigerians who lose money to these schemes cannot. They're navigating a brutal cost-of-living environment, reached on platforms they use daily, presented with content that looks and sounds like someone they have reason to trust.
Interpol's 2026 report identifies Business Email Compromise (BEC) as the "financial engine of transnational cybercrime" [4]. According to the report, threat actors based in Nigeria, active since 2021, have become central to international money-laundering operations, routing illicit proceeds through crypto exchanges, shell companies, and mobile payment platforms [4].
The sophistication of BEC campaigns has increased dramatically, with AI now used to generate highly convincing email correspondence that mimics executive tone, internal jargon, and signature styles with near-perfect fidelity [4]. In 2025, TrendAI data indicated that 70% of BEC detections originated in South Africa and 29% from Nigeria [4].
Crypto transactions represent another major threat vector. Interpol's report notes that crypto transactions valued at $205 billion occurred between July 2024 and July 2025, with Nigeria accounting for $92 billion of this figure [4]. Investors in Nigeria have become targets of fraud, and South Africa, Kenya, and Nigeria were identified as countries with the highest vulnerabilities for data breaches [4].
Nigeria has three frameworks theoretically applicable to deepfake fraud, none designed with the technology in mind. The Cybercrimes (Prohibition, Prevention, etc.) Act 2015, as amended in 2024, penalizes fraud, identity theft, phishing, and dissemination of false information through computer systems. Section 32 criminalizes impersonation in electronic communications with intent to commit fraud.
But the Act does not mention artificial intelligence, deepfakes, or synthetic media. It does not contemplate a scenario where the impersonation is not a human pretending to be someone else, but a machine generating a convincing simulacrum of them. This gap is not a technicality—it's a substantive enforcement problem.
The Nigeria Data Protection Act 2023 protects personal data, including biometric data and, by implication, a person's likeness. The unauthorized use of someone's face could theoretically be prosecuted. But stretching laws designed for a different era creates uncertainty.
Fatogun has urged policymakers to modernize legal frameworks to address emerging digital threats and to introduce mandatory AI governance standards for banks and fintech companies, including stress-testing systems against deepfake attacks and AI-enabled fraud simulations [6]. He has called for an amendment to the Cybercrimes Act to explicitly criminalize deepfake fraud, voice cloning, and automated phishing [6].
The Senate is currently amending the Cybercrime Act to guarantee a secure environment for the digital economy, with Senator Shuaib Salisu, Chairman of the Senate Committee on ICT and Cyber Security, noting that "an unprotected digital space is like a major highway left vulnerable to armed robbers."
Despite AI's power, human analysts remain essential. Fraud detection is an adversarial problem: fraudsters constantly experiment with new techniques designed to exploit weaknesses.
In Oxygen X's case, the proactive approach has allowed analysts to spend less time investigating low-quality alerts and more time focusing on genuine risk. The company has been able to scale operations without increasing headcount, as centralized workflows and improved behavioral visibility reduce investigation time per case.
Fatogun emphasizes that AI does not replace human judgment but amplifies it [6]. He argues that "the future of fraud prevention lies in adaptive intelligence systems that continuously learn and evolve," and that institutions must embrace "predictive analytics, behavioral biometrics, anomaly detection, and forensic AI tools capable of identifying suspicious digital footprints in real time" [6].
No single institution can effectively tackle digital fraud alone—making collaboration among fintech firms, banks, regulators, and law enforcement agencies increasingly important.
Fatogun has called for the establishment of a coordinated national framework involving the Central Bank of Nigeria, the Economic and Financial Crimes Commission (EFCC), cybersecurity agencies, telecom operators, and law enforcement agencies to address the growing threat of AI-driven fraud [6]. He noted that while Nigeria's digital economy continues to expand rapidly, cybersecurity readiness remains uneven across institutions, particularly among smaller fintech firms that may prioritize growth over compliance infrastructure [6].
Cybersecurity is becoming an ecosystem-wide responsibility. As fraud becomes more coordinated and opportunistic, growth strategies built purely around speed are proving harder to sustain. The emerging lesson is that risk management is no longer just a defensive function—it is becoming a prerequisite for sustainable scale.
Fatogun also identifies insider collusion as a major factor in cyber-enabled fraud, noting that many successful financial crimes involve either deliberate or negligent actions by insiders [6]. He advocates stronger employee vetting processes, improved access controls, segregation of duties, and enhanced forensic auditing practices to address such risks [6].
The first line of defense is healthy skepticism. Fatogun warns: "Nigerians must learn not to trust urgency, emotional pressure, or digital familiarity. If someone calls claiming to be from your bank, verify independently. Do not rely on caller ID because AI voice spoofing and cloned identities are now possible" [6]. He also advises people to avoid oversharing personal information online, warning that fraudsters increasingly rely on publicly available data to create convincing scams [6].
AI systems are only as good as the data they're trained on. Poor-quality data can create bias that makes systems actively harmful, flagging legitimate transactions from certain demographic groups while missing actual fraud.
Fintechs in Nigeria identify several obstacles to large-scale AI adoption. According to the CBN's Fintech Report 2025, 37.5% of respondents cite shortages of technical talent and regulatory uncertainty as major constraints, while 50% consider access to high-quality data and adequate infrastructure as the most critical condition for AI development [5].
The Nigerian Society of Physical Sciences Journal has published research highlighting the importance of advanced techniques for addressing data quality challenges in fraud detection. One study focused on a hybrid sparse autoencoder–isolation forest framework for real-time credit-card fraud detection, demonstrating how unsupervised learning methods can effectively handle the imbalanced nature of fraud datasets [2]. The research references global studies on hybrid deep learning frameworks that use synthetic oversampling and attention mechanisms to improve detection accuracy [2].
The report highlights the role of public digital infrastructure, including digital identity systems and data-sharing frameworks, in supporting AI development. As the CBN noted, "as AI systems move from experimental tools to core components of financial services, governance and supervisory learning must evolve alongside industry adoption."
According to the Central Bank of Nigeria's Fintech Report 2025, a staggering 87.5% of Nigerian fintech companies now deploy AI for fraud detection. This makes it the dominant use case, far outpacing applications like customer service chatbots at 62.5% [5].
Yet governance lags behind. The report estimates that as AI becomes a core financial services tool, supervisory and governance capacities will need to evolve at the same pace [5]. Nearly 62.5% of respondents say they show strong interest in participating in an AI-focused regulatory sandbox, while 75% rank ethical and transparent AI use as a priority in credit decision-making and risk management [5].
Days after the GenAI Learning Concepts Ltd launch in Lagos in February 2026, the CBN formally wrote AI into its anti-money-laundering framework, requiring banks, fintechs, and payment companies to deploy automated AML systems powered by AI and machine learning [7]. Bashir Yusuf, dLocal's Nigeria Country Manager, noted that "the direction of travel is clear: Nigeria wants smarter compliance, not just more of it" [7].
dLocal, a technology company that bridges global digital platforms with consumers in emerging markets, has already embedded AI across three layers of its operations: fraud prevention and security (reducing false alerts by up to 90%), smart routing (lifting approval rates from 80% to 95%), and AML compliance (using AI-driven monitoring that continuously learns from data and investigator feedback) [7].
Nigeria is moving with unprecedented speed to establish legal and regulatory guardrails. The most significant development is the National Digital Economy and E-Governance Bill, which is currently in its final legislative stage. The bill aims to modernise Nigeria's legal framework by granting digital signatures and electronic records the exact legal weight as traditional paper documents.
Nigerian fintechs operating AI fraud detection systems must satisfy two regulatory regimes simultaneously:
The CBN Fintech Report 2025 found that 87.5% of respondents said the cost of meeting regulatory and risk requirements significantly affects their ability to innovate [5]. Fintechs must carefully weigh the investment required for AI fraud detection systems.
The Moniguard project demonstrates the economic viability of custom solutions. With a monthly cost of approximately $2,500, the system prevents ₦195 million in monthly fraud, delivering a 77,000% ROI [9]. For fintechs processing high transaction volumes, investment in AI-powered fraud detection can deliver significant returns.
Fraud patterns change weekly, and new scam types appear constantly. AI models need regular retraining on fresh data, including feedback from fraud analysts' decisions. As the CBN noted, the fintech sector widely adopts AI, mainly for risk management and operational efficiency, with fraud representing a "major issue for the industry."
The Moniguard project includes automated retraining pipelines with feedback-driven model improvement, feature stores (Feast + Redis) for consistent feature serving, A/B testing with canary deployments, and model monitoring with drift detection and performance tracking [9].
As AI models grow in complexity, the "black box" problem emerges—it becomes difficult to explain why a specific transaction was flagged. This creates compliance risks and erodes trust.
The Moniguard project addresses this through SHAP explanations for every decision, ensuring that each detection is accompanied by feature importance visualization [9]. This makes AI decisions explainable and auditable, which is essential for regulatory compliance.
The CBN's Fintech Report 2025 notes that 75% of fintechs prioritize ethical and transparent AI use in credit decision-making and risk management [5]. This suggests industry recognition that explainability and transparency are compliance prerequisites.
The fraud landscape is becoming more adversarial. Criminal groups are evolving faster, deploying AI tools at scale, and targeting both technology and human psychology. The fight is increasingly about who can adapt more quickly.
Interpol's 2026 report warns that AI is increasing the speed of cyberattacks, making them more scalable, and increasingly difficult for victims and platforms to detect [4]. Ransomware detections in Nigeria reached 5,822 in 2025, reflecting the country's dual role as an origin and target of cybercrime [4].
Deepfake technology is becoming more sophisticated and accessible. When someone can impersonate a public figure or CEO using tools readily available online, the entire foundation of identity verification needs rebuilding.
No single institution can effectively tackle digital fraud alone—making collaboration among fintech firms, banks, regulators, and law enforcement agencies increasingly important. The CBN has outlined plans to establish a Security Operations Centre and a national fraud intelligence-sharing platform to improve threat detection and coordinated responses across the financial sector.
Fatogun recommends the establishment of a National AI Cybercrime Intelligence Centre to coordinate the efforts of anti-graft agencies with advanced equipment, such as digital forensics tools, to track crypto-laundering and dark web activities [6]. He also emphasizes the need for the National Assembly to amend the Evidence Act to establish clear standards for authenticating and validating AI-generated or digital criminal evidence in court [6].
As fraud becomes more coordinated and opportunistic, growth strategies built purely around speed are proving harder to sustain. The emerging lesson is that risk management is no longer just a defensive function—it is becoming a prerequisite for sustainable scale.
AI-powered fraud detection represents a fundamental shift from reactive, rules-based security to adaptive, intelligence-driven protection. For Nigeria's rapidly expanding fintech ecosystem, AI-led security is no longer a future investment but a present necessity.
Academic research confirms the economic imperative: the African Banking and Finance Review Journal study found that AI-driven fraud detection has a positive and significant impact on Nigerian banks' ROI [1]. Hybrid AI models integrating Graph Neural Networks and Isolation Forest algorithms have achieved F1-scores of 0.92 and AUC-ROC of 0.98, significantly outperforming traditional approaches [2].
Key takeaways:
As cyber threats become faster, smarter, and more coordinated, companies that combine intelligent technology, strong governance, and ecosystem collaboration will be best positioned to protect customers and sustain confidence in Nigeria's digital financial system.
The question is no longer whether AI will change financial crime—it already has. The question is whether we're building defenses that can match the speed of people who treat fraud like a technology startup. The answer will determine the future of Nigeria's digital economy.
Academic journals, research reports, media articles, and technical documentation cited in this article.