5 key layers of defence against transactional fraud

The rapid adoption of instant payments, the digitalisation of financial services, and the increasing sophistication of cybercriminal techniques have created an environment conducive to rising fraud.

Transactional fraud reaches record figures

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ccording to a FICO report¹, in 2024, 80% of Spanish consumers were targeted by scams related to instant payments — a 4% increase compared to the previous year. Moreover, 7% of victims lost over €5,000, nearly doubling the 4% figure recorded in 2023. On a global scale, TransUnion reported that 5.2% of all attempted digital transactions in the first half of 2024 were suspected of being fraudulent². In Spain, 4.11% of online transactions were flagged as suspicious, with account takeovers, credit card fraud, and identity theft among the most common threats. 

This scenario calls for an evolved approach to digital security. The ideal digital identity platform — particularly in fraud-prone sectors like banking, fintech, insurance, eCommerce or public services — must go beyond a single control point. Instead, it should be structured around five key pillars that, when combined, provide a robust and adaptive defence against transactional fraud. Each of these pillars represents a protective layer that operates at different stages of the digital interaction lifecycle. Below, we explore these five essential foundations. 

Layer 1: Initial shield — Advanced digital identity verification 

The first line of defence against transactional fraud is preventing a fake or stolen identity from entering the system. This layer includes technologies such as facial biometrics, liveness detection, smart OCR, and AI-driven document validation. Its role is especially critical in the face of modern threats like deepfakes, which allow attackers to simulate hyper-realistic human faces to bypass traditional biometric controls. 

But this goes beyond onboarding. Continuous and contextual authentication strengthens this defence over time, enabling the detection of unusual access patterns or account takeover attempts. Continuous authentication involves constantly verifying that the user remains who they claim to be — not just at login, but throughout the entire session. 

Contextual authentication, meanwhile, analyses factors such as device, geographic location, time of day, and type of operation to adjust security levels dynamically based on real-time risk. 

Layer 2: Early detection — Behavioural monitoring and risk signals 

Once the user is inside the system, silent surveillance begins. This layer acts as a radar for digital activity and uses: 

• Passive biometrics, analysing traits like typing speed or mouse movement. 

• Navigation pattern analysis, tracking the typical user journey within the platform. 

• Device fingerprinting, identifying unique device attributes (browser, OS, screen resolution, etc.). 

• Geolocation, detecting whether access originates from an expected or unusual location. 

Machine learning enables these systems to adapt behavioural baselines to each individual user and identify anomalies that may indicate fraudulent access. 

Layer 3: Active containment — Real-Time intelligent control 

During the transaction process, this layer determines whether to allow, block, or strengthen authentication. It relies on a decision engine that cross-checks variables such as amount, destination, frequency, channel, device, and time of day. This layer also enforces anti-fraud rules and predictive models to trigger adaptive authentication only when the level of risk justifies it — balancing security with user experience. 

Layer 4: Analytical response — Post-transaction monitoring and learning 

The analysis doesn’t end with a click on “confirm”. This layer scrutinises post-event activity, identifies fraudulent operations that may have bypassed previous filters, and feeds key insights back into the system. It includes forensic analysis tools, alert correlation, automated incident investigation, and pattern visualisation. Its aim is to close the defence loop and enhance future responses. 

Layer 5: Human fortification — User awareness and anti-fraud UX 

The end user is both a target for fraud and a potential ally in its detection. This layer aims to empower users through educational messages, proactive alerts, clear warnings during suspicious transactions, and UX flows designed to prevent errors or manipulation. It also includes protection against authorised push payment (APP) scams and the promotion of safe digital habits. 

An integrated security architecture that covers the full fraud lifecycle 

Defending against transactional fraud — understood as the attempt to carry out unauthorised transactions, impersonate identities, or manipulate legitimate operations in digital environments — does not rely on a single technology or solution. This type of fraud particularly affects sectors such as banking, fintech, e-commerce, insurance, and digital public services, where exposure to risk is constant. 

That is why coordinating all these layers — each acting at different stages of the process — is essential. Securing identity, monitoring behaviour, responding in real time, conducting post-event analysis, and empowering the user are complementary steps in a modern anti-fraud architecture. Only by combining them can organisations effectively respond to a dynamic, persistent, and increasingly automated threat landscape. 

If your organisation needs to define or strengthen its digital verification strategy, TrustCloud can help you design a tailored approach — one that is aligned with your risk level, type of operations, and industry sector. 

Get in touch to discover how we can help you build a secure and frictionless defence against fraud 

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¹ El 80% de los consumidores ha sufrido intentos de estafa en pagos instantáneos en 2024 (Spanish) | Mari Rodríguez. Muy Pymes. January, 2025 

² Actualización H2 2024: Informe sobre el fraude omnicanal (Spanish)| TransUnion