Nowadays, the banking sector is undergoing a major digital transformation. The rise of digital channels has led not only to a surge in transaction volumes but also to an explosion in the attack surface for fraudsters. More than a one-off issue, fraud has become a central challenge of trust, compliance, and resilience. The key question this article addresses is:
How can African financial institutions shift from reactive detection to proactive fraud monitoring through AI and predictive analytics?
This issue is directly linked to the challenges faced by institutions undergoing digital transformation: the multiplication of channels (mobile banking, instant payments, digital onboarding) and therefore higher fraud risks; compliance, risk, and IT teams often limited by fragmented or purely reactive tools; and the growing need for intelligent, integrated, and locally adapted tools capable of detecting anomalies as they emerge or even anticipating them.
Key issues to address:
- How to build proactive prevention rather than endure attacks;
- How to leverage behavioral analytics and AI to detect weak signals;
- How to adopt a 360° vision of customer and transactional data to break down silos and ensure effective implementation.
1. State of banking fraud and the limits of reactive detection
Banking fraud is constantly evolving: the scale of diversions, digital vectors, and the use of increasingly sophisticated technologies (AI, deepfakes, mule networks, etc.) are changing the landscape.
IBM defines fraud detection as “the process of identifying suspicious activity that indicates criminal theft of money, data, or resources might be underway.” It is commonly performed by fraud detection software that monitors transactions, applications, APIs, and user behavior.
According to IBM’s report “Banking in the AI Era: The Risk Management of AI and with AI”, 61% of banking executives identify fraud detection as the area where AI can deliver the most value.
According to Market Growth Reports, the Fraud Detection and Prevention Market was valued at USD 40,725.21 million in 2025 and is expected to reach USD 160,234.35 million by 2034, growing at a CAGR of 16.44% from 2025 to 2034.
The MEA (Middle East and Africa) market stood at USD 3,225.21 million in 2025, with a 7.8% share, and is projected to reach USD 12,734.35 million by 2034, growing at a CAGR of 16.1%.
Limits of a reactive approach
Traditional fraud detection systems often rely on static rules (thresholds, predefined scenarios) and only intervene after fraud has caused damage. This logic leads to several weaknesses:
- Reaction times that are too long, allowing the fraudster to operate before detection;
- Data and channel silos: attacks can come from unsupervised or uncorrelated channels, allowing fraudsters to exploit weak entry points;
- Slow adaptation to emerging tactics: fraudsters now use generative AI, deepfakes, and “fraud-as-a-service,” making reactive approaches obsolete.
According to CoinLaw, banks adopting behavioral AI have observed a 30% reduction in false positives.
For African financial institutions, these dynamics are especially relevant: the rapid rise of mobile banking, strong digital penetration (often without fully mature control infrastructures), and the multiplication of channels all increase exposure. Reactive detection is no longer sufficient and creates a growing gap with organized fraud networks.
Recommendation: Nexfing recommends conducting a maturity audit: assess real-time monitoring capabilities, measure false positive rates, verify multichannel data integration, and evaluate the speed of detecting emerging patterns. This approach will help lay the foundations for a proactive model.
2. Why adopt proactive monitoring?
Several factors drive banks toward proactive monitoring:
- Increasing sophistication of attacks: fraudsters use AI, deepfakes, and synthetic identities. McKinsey’s “How Agentic AI Can Change the Way Banks Fight Financial Crime” highlights the rise of “agentic AI” capable of automatically detecting, suggesting, and responding to fraud.
- Volume and speed of digital transactions: with instant payments and mobile banking, transaction volumes are exploding – batch analysis is no longer enough. McKinsey notes that players must “build continuous monitoring systems.”
- Regulatory and reputational pressure: customers expect a smooth yet secure experience, while regulators demand proof of anticipation.
- Emergence of new detection technologies: according to Gartner’s “Online Fraud Detection Reviews and Ratings” ,online fraud detection (OFD) solutions now cover malicious bots, account takeovers (ATO), and must integrate with mobile channels.
A proactive monitoring approach enables key gains:
- Reduced fraud losses: institutions using behavioral AI and multichannel correlation report significant improvements. IBM’s “AI Fraud Detection in Banking” cites a 6% improvement with an LSTM model and 10% with real-time AI.
- Better customer experience: fewer false blocks, less friction, more trust.
- A risk/compliance function transformed into a strategic driver rather than a control gatekeeper.
- Greater resilience through anticipation.
Challenges to overcome:
- Data quality, availability, and integration: many African institutions still operate in silos with inconsistent data.
- Technology and skills: AI, graph analytics, and real-time correlation require expertise, processes, and budgets.
- AI governance and compliance: AI models must be explainable, auditable, and monitored.
- Organizational adoption: aligning IT, compliance, business, and a monitoring culture.
In the African context, these challenges are amplified but not insurmountable. The acceleration of digitalization and the need for trust in financial institutions offer a strategic opportunity: moving early to proactive monitoring can be a key differentiator. For IT and security teams, this means rethinking architectures, data flows, algorithms, and interdepartmental collaboration.
Recommendation: Define a clear roadmap for proactive monitoring: identify critical channels, define “early warning signals,” launch a pilot on high-risk segments, and scale progressively. Focus on data (integration, quality), AI (adaptive algorithms), and organization (compliance, risk, IT).
3. Proactive prevention: implementation
Proactive prevention aims to detect and block fraud before it materializes into losses or incidents. It relies on several key levers:
- Real-time monitoring of transactions and logins, not just batch analysis — for example, instant alerts on unusual transfers or account changes without prior history.
- Definition of weak signals: atypical behaviors (unusual remote login, rapid address change, new card used abroad, etc.).
- Deployment of automated or semi-automated “kill switches” (transaction blocking, additional verification, escalation).
- Internal and external collaboration (fraud, compliance, IT, banks, regulators, payment networks) to enrich data and identify emerging trends.
Proactive prevention shifts a bank’s posture from “reacting too late” to “anticipating and blocking before damage.” For strategic decision-makers (CIO, CDO, CTO) and IT leaders, it means embedding continuous monitoring layers, streaming data pipelines, adaptive rules, and operational alert dashboards into existing architectures.
The main challenge is calibrating thresholds and algorithms to avoid degrading customer experience (e.g., wrongly blocking legitimate users). The right balance between security and fluidity is crucial.
Recommendation:
Start with a high-risk segment (e.g., digital onboarding, international transfers, mobile banking) and deploy a real-time monitoring engine with tailored rules and scoring. Measure gains (loss reduction, false positives), refine rules, and scale up. Establish clear KPIs (detection time, false positive rate, avoided losses).
4. Behavioral analytics and AI: techniques, architecture, algorithms
The shift to proactive monitoring relies heavily on AI and behavioral analytics. Key technical components include:
- Customer behavioral profiling: building a user “digital fingerprint” (transaction history, channel usage, geolocation, device fingerprinting). Any deviation becomes an anomaly signal.
- Machine learning and graph analytics: to detect fraud networks (mule accounts, relational clusters). IBM describes a “FraudGT” (Graph Transformer) model that improves fraud detection in transaction graphs.
- Deep learning / Auto-encoders / Mixture of Experts: a 2025 Cornell University study used a hybrid model combining RNNs, Transformers, and auto-encoders, achieving 98.7% accuracy, 94.3% precision, and 91.5% recall.
- Predictive monitoring / weak signal detection: McKinsey highlights “agentic AI” automating end-to-end antifraud processes.
- Real-time orchestration: data ingestion, scoring, alerting, analytical investigation, automated blocking.
The crucial shift is moving from static rules to adaptive models. AI can absorb the growing variety of fraud schemes but requires rigorous data cleaning, strong governance (explainability, compliance, bias control), and well-trained teams. Otherwise, the risk is ending up with a poorly governed AI black box.
In African markets, data fragmentation and limited expertise are common. A “cloud + technology partnership” strategy can accelerate implementation.
Recommendation: Adopt a hybrid strategy: start with supervised and unsupervised AI engines on a limited scope (e.g., high-value mobile transactions), then expand. Establish clear AI governance processes (model monitoring, retraining, bias auditing). Involve fraud analysts in model co-design to ensure business alignment.
5. A 360° view of customer and transactional data: integration, unification, governance
Effective monitoring requires a unified data view: customer, transaction, device, channel, geolocation, relational network. Key axes include:
- Breaking down silos: consolidate online, mobile, branch, payment, and KYC/AML data into a unified platform. This enables detection of cross-channel or hybrid fraud (e.g., account opened in branch, used online, then transferred abroad).
- 360° customer view: build complete profiles (identity, behaviors, history) to identify unusual deviations.
- Data quality and governance: ensure consistency, completeness, and freshness of data, with clear access, compliance, and traceability rules.
- Link analysis / graph data: map relationships between accounts, devices, and entities (mules, linked cards) to detect suspicious clusters.
- Investigation orchestration: centralize alerts, enrich them with customer data, connect to fraud teams, and define escalation workflows.
Having a 360° view is a competitive advantage. While many still operate in silos, those who integrate their data gain a decisive edge over fraudsters. From an IT and security standpoint, this often involves rearchitecting toward a data lake or enrichment platform and aligning data engineering, security, compliance, and business teams.
The main risk is launching a “big picture” vision without a concrete pilot, which can lead to delays, overspending, and endless projects. It’s better to start small and expand gradually.
Recommendation: Nexfing recommends defining a “360° pilot use case” (e.g., mule account detection or high-risk digital onboarding) and architecting a data platform aggregating at least customer + transaction + device/channel data. Establish light but functional governance (quality, access, roles). Then, deploy relational analysis and integrate it into the AI scoring engine.
6. Global Recommendations for African Financial Institutions
- Prioritize investments in real-time detection and adaptive AI rather than reactive solutions
Move from static rule-based detection toward dynamic, AI-driven systems capable of analyzing transactions and behavioral patterns in real time. Adaptive models continuously learn from new fraud tactics, minimizing detection delays and reducing operational losses.
- Establish an inter-channel control center (fraud, risk, IT)
Create a centralized hub that unifies fraud intelligence across mobile, web, branch, and transfer channels. This “nerve center” enables end-to-end visibility, rapid escalation, and consistent decision-making—essential to counter complex cross-channel fraud schemes.
- Build a consolidated data platform compatible with AI (data lake or lakehouse)
Ensure all transactional, behavioral, and customer data is aggregated and governed in a single, high-quality repository. This foundation is key to enabling advanced analytics, ensuring data lineage, and facilitating AI deployment at scale.
- Develop hybrid AI models (supervised + unsupervised) and relational graph analytics
Combine supervised learning (based on known fraud cases) with unsupervised models (detecting anomalies) to uncover emerging patterns. Graph-based analysis helps identify mule networks, synthetic identities, and collusive behaviors that often escape traditional systems.
- Introduce behavioral monitoring and early alerting mechanisms
Implement continuous profiling of users, employees, and third parties to detect subtle deviations or weak signals that precede fraudulent activity. Behavioral scoring strengthens predictive capabilities while limiting false positives.
- Integrate AI governance and compliance transparency
Adopt a structured governance framework for AI: model validation, explainability, traceability, and regulatory alignment (KYC, AML, GDPR). Transparent scoring and auditability foster trust from both regulators and customers.
- Run accelerated pilot programs on high-risk use cases
Before full-scale deployment, prioritize pilot initiatives on critical segments such as digital onboarding, mobile payments, or international transfers. This allows faster ROI demonstration, operational fine-tuning, and cross-departmental alignment.
- Cultivate a culture of anticipation through targeted training
Develop continuous learning programs for fraud, compliance, and IT teams on emerging threats—deepfakes, synthetic IDs, or Fraud-as-a-Service models. Building internal vigilance and adaptability is as critical as the technology itself.
- Strengthen collaboration and intelligence sharing
Partner with regulators, peer institutions, and payment networks to exchange anonymized fraud indicators and feed AI models with collective intelligence. A shared approach amplifies detection precision and reduces systemic vulnerabilities.
- Measure and steer performance through adapted KPIs
Track operational impact through measurable indicators: average detection time, avoided losses, false positive reduction, ROI of detection systems, and rate of investigations successfully closed. These metrics ensure data-driven improvement and governance accountability.
In conclusion, African financial institutions can successfully move from reactive fraud detection to proactive monitoring by relying on:
- Proactive prevention (real-time monitoring, preemptive actions);
- Behavioral analytics and AI (adaptive models, graph analytics, agentic AI);
- A 360° view of customer and transactional data (integration, unification, governance).
With an integrated technological architecture, appropriate governance, and an anticipation-oriented organization, banks do more than detect fraud, they become resilient, earn customer trust, and position themselves as transformation leaders rather than attack victims. Risk doesn’t disappear it evolves but by adopting a proactive posture, institutions become sentinels rather than targets.
You may be wondering “Are we ready to anticipate the next fraud before it strikes?”
At Nexfing, expertise covers fraud and monitoring maturity audits, custom AI/Blockchain platform design, real-time solution integration, and behavioral supervision.
Contact us to build your roadmap toward proactive fraud monitoring and secure your digital transformation.
Sources :
IBM :
https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/banking-in-ai-era
https://www.ibm.com/think/topics/fraud-detection
https://www.ibm.com/think/topics/ai-fraud-detection-in-banking
Market Growth reports :
https://www.marketgrowthreports.com/market-reports/fraud-detection-and-prevention-market-115401
CoinLaw :
https://coinlaw.io/banking-fraud-detection-statistics
McKinsey :
Gartner :
https://www.gartner.com/reviews/market/online-fraud-detection
Academic research from Cornell University: