What is a cyber-fraud fusion model? From session visibility to real-time threat prevention

In digital banking, fraud prevention can no longer wait until the transaction. Modern gangs run blended operations. Social engineering creates the opening. Cyber techniques keep access and hide the trail. Payments and laundering finish the job. It’s a single campaign that moves across people, devices, channels, and time.

This fusion of tactics has changed the nature of fraud. Attackers no longer rely on a single entry point or moment. They prepare, observe, and manipulate before the transaction even begins. Traditional fraud tools that trigger alerts only at the point of transaction can’t keep pace with this shift.

The cyber-fraud fusion model changes this. By merging cybersecurity telemetry with fraud intelligence, banks gain real-time visibility across the entire digital session, empowering teams to detect, understand, and contain threats as they unfold.

This article examines the impact of this new approach on banking security and its implications for reshaping fraud management within banks and financial institutions. 

As digital channels dominate customer interactions, they’ve become the prime hunting ground for fraudsters. Modern attacks no longer rely on a single tactic: cybercriminals combine malware, phishing, Remote Access Tools, and social engineering to infiltrate sessions long before a transaction occurs.

These hybrid attacks blur the line between cyber intrusion and financial fraud. A customer may appear to log in legitimately, yet behind the scenes, a fraudster might be guiding their every click through an overlay attack or hijacked session.

This convergence of cyber and fraud means one thing: banks need visibility beyond the transaction layer. They need a model capable of recognising threats as they form - not after they strike.

Enter the cyber-fraud fusion model.

A cyber-fraud fusion model unites two worlds that, until recently, operated in silos: cybersecurity and fraud prevention.

• Cybersecurity signals: device intelligence, session behaviour, network telemetry.
• Fraud analytics: transaction patterns, behavioural data, and risk scoring.

By correlating these streams, the model produces a continuous storyline of the digital session, revealing context that isolated systems miss.

Think of it as seeing a locked front door and what happens inside the house. Instead of focusing only on the “transactional moment,” the fusion approach watches how the entire interaction evolves - detecting subtle anomalies that perimeter defences or credential checks would deem valid.

Traditional fraud systems rely on rule-based thresholds or machine learning models trained on past data. They focus on transactions, not sessions. The problem is that most modern fraud begins before the transaction - through man-in-the-browser attacks, overlay scams, session hijacking, or remote access manipulation.

These threats bypass perimeter checks entirely, leaving fraud teams to clean up after the fact. The result is slower detection, higher losses, and frustrated customers.

The cyber-fraud fusion model flips this approach by shifting detection left, identifying anomalies as soon as they appear in live traffic. 

By monitoring behaviour in real time and linking cyber indicators with fraud context, banks can stop fraud earlier, reduce false positives, and preserve the customer experience.

A fusion model combines several analytical layers into one continuous detection loop:

1. Session telemetry – Real-time tracking of device, browser, app, and network data to detect compromise indicators.
2. Behavioural analytics (UEBA) – Analysing how users interact, spotting deviations from normal behaviour.
3. Transaction risk engine – Scoring risk contextually, not in isolation.
4. Threat intelligence – Mapping tactics, techniques, and procedures (TTPs) attackers use.

Together, these layers construct a unified attack narrative, correlating what’s happening across channels and sessions.

At Cleafy, we bring a cybersecurity mindset to fraud prevention, focusing on how attacks unfold, not just when they trigger. Our platform monitors full user sessions in real-time, from initial access to logout, to reveal technical indicators of compromise such as session manipulation, remote access, and malware injection. 

These are the mechanics that often enable scams to succeed, even when the customer appears to be acting normally.

We don’t claim to detect social engineering itself. Instead, we detect the digital consequences it creates; the traces, connections, and anomalies that expose how fraudsters turn persuasion into action.

We see AI as an operational amplifier, not a standalone solution. It helps analysts connect evidence faster, highlight related cases, and focus their attention where it matters most. Its value comes from the quality of the signals it works with: explainable, correlated data that can be traced and trusted.

For banks, this approach changes the rhythm of defence. Instead of reacting after an incident, teams can act earlier in the attack sequence, containing threats before money moves. It’s a shift toward prevention powered by visibility. We’re championing that transformation.

The future of AI in fraud detection isn’t about training smarter models. It’s about fusing cybersecurity intelligence with fraud management to detect attacks before they become fraud. Read our latest article "The next frontier of AI in fraud detection: Why cyber-fraud fusion beats model fatigue" to learn more

With a shift-left detection strategy, threats are contained before funds move or credentials are stolen.

AI and automation tools - such as Cleafy’s Copilot - further enhance analyst productivity by surfacing insights, correlating events, and suggesting response actions in real time.

Adopting a cyber-fraud fusion model is not only a security upgrade; it’s a business enabler.

Key benefits include:

• Early detection - identifying fraud in progress before money moves.
• Reduced false positives - smoother customer journeys and fewer blocked transactions.
• Faster investigations - thanks to shared visibility and correlated session data.
• Lower operational cost - by automating detection and response.
• Improved compliance - aligning with PSD2 and EBA guidelines for risk-based authentication.

Imagine detecting a session anomaly where a user’s browser suddenly executes hidden scripts after login. Instead of a delayed alert, the system flags the session in real time, enabling the bank to intervene before the fraudster transfers funds.

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Implementing a cyber-fraud fusion model presents its own challenges. 

The first is data integration, which involves bringing together cyber telemetry and fraud signals in a way that allows for real-time, contextual analysis at scale. This is technically complex. 

Equally demanding is achieving real-time correlation at scale - banks must process millions of sessions across multiple devices and channels without losing performance or accuracy.

Cultural change can be just as significant as technical change. Cyber, fraud, and digital teams often operate in silos, each with different priorities and workflows. Moving towards a fusion model requires collaboration, shared goals, and a unified operational mindset. 

Another barrier is the explainability of AI. For compliance and audit purposes, every automated decision must be transparent and understandable to both analysts and regulators.

Overcoming these challenges starts with small, measurable steps. Banks can begin by launching pilot projects focusing on a specific channel or use case, allowing them to test the model’s effectiveness in a controlled environment. 

Building cross-functional teams helps align cyber and fraud specialists around shared visibility and faster response times. Establishing full session visibility is crucial - institutions need to identify where their blind spots lie and work to close them. 

Finally, working with an experienced partner such as Cleafy and its Threat Intelligence Team can accelerate implementation, providing the practical expertise and proven frameworks needed to successfully operationalise the fusion model.

As banks move toward open banking, embedded finance, and digital wallets, fusion models will scale to protect emerging channels. The next frontier will be intelligence sharing across institutions, strengthening collective defence through shared visibility into attacker behaviour.

The future isn’t just about detection; it is proactive prevention.

Building a roadmap for proactive fraud prevention

For banks ready to modernise fraud defences, here’s how to begin:

1. Assess detection gaps - identify where fraud still slips past existing systems.
2. Map session visibility - understand what telemetry is available and identify any remaining blind spots.
3. Pilot a cyber-fraud fusion model - partner with a trusted vendor to test early detection at the session level.

By uniting cyber and fraud disciplines under a shared strategy, banks can stay ahead of modern threats and move from chasing fraud to predicting it weeks or days before it happens.