Omnichannel Fraud Detection: Why Multichannel Isn't Enough

How Omnichannel Detection Prevents Fraud

Imagine this:

A fraudster steals your credit card details online and uses them to make a purchase on an e-commerce site. The merchant's fraud detection system checks the transaction amount, merchant ID, and location but detects nothing unusual.

Meanwhile, your bank, which issued the card, has its own fraud detection systems. However, those systems have no visibility into the fact that, just a few hours earlier, someone accessed your account from a new device and extracted your CVV code. The transaction is approved, and the fraudster successfully completes the purchase.

And now imagine this:

A customer receives a phone call from someone claiming to be from their bank. The caller explains that a suspicious €1,000 purchase has been detected and accurately describes the transaction, including the amount, date, and merchant. Convinced the caller is legitimate, the customer is asked to approve a push notification in their banking app to reverse the payment.

In reality, the caller is a fraudster who has already gained access to the customer's account using stolen credentials and is now persuading the customer to authorise a fraudulent transaction.

In both examples, the fraud may not be identified as suspicious because each event appears legitimate when viewed in isolation.

The push approval seems legitimate
The device matches your own
The transaction doesn’t exceed any limits
And the call? That happens on a completely different channel - one that no fraud detection system is monitoring.

These examples illustrate cross-channel fraud, where attackers exploit disconnected customer interactions across multiple touchpoints. In both cases, the underlying fraud detection systems operate within a traditional multichannel architecture, making it difficult to correlate related events and identify the attack as it develops.

The Limitations of Multichannel Fraud Detection

Most financial institutions and solution providers operate in what is called a multichannel architecture. This means they have tools covering various areas of the customer journey: onboarding, transaction monitoring, session behavior, card payment analysis, etc. These tools are often best-in-class in their respective domains, but they don’t communicate effectively.

Here’s how it typically works:

• A digital onboarding product collects biometric data and device fingerprints.
• A separate session monitor flags anomalies like unfamiliar geolocations or rapid mouse movements.
  
• A transaction tool analyzes payments based on sender, recipient, and amount.
  
• A card fraud tool looks at spending patterns and merchant types.
  
  

Each of these systems may generate valuable fraud signals, but those signals are rarely correlated across channels. At best, limited integrations allow one system to trigger another, creating a linear workflow rather than a unified view of customer activity. As a result, fraud signals that span multiple touchpoints or stages of the customer journey often remain disconnected, making complex attacks much harder to identify.

The earlier examples demonstrate these limitations.

The first example illustrates Card-Not-Present (CNP) fraud, where banks and merchants each have fraud controls in place but operate independently. Without correlating signals across systems, critical indicators remain disconnected, creating opportunities for fraudsters.

The second example demonstrates a Scam Phish Attack, where criminals combine multiple communication channels - including email, SMS, voice calls, and push notifications - to manipulate victims into authorising fraudulent transactions. Viewed independently, each interaction appears legitimate. Together, they reveal a coordinated attack.

Going back to the examples:

The first example is the reality of Card-Not-Present (CNP) fraud, and it’s a prime example of what happens when channels don’t talk to each other. It’s not that banks and retailers don’t have systems in place; it’s that those systems operate in isolation. This fragmentation leaves significant gaps in fraud detection, and fraudsters know it.

The second example is a Scam Phish Attack, a sophisticated evolution of phishing where criminals exploit multiple communication channels (email, SMS, voice calls, and push notifications) in real time. It’s not just about stealing login credentials; it’s about manipulating victims into actively participating in fraud.

Omnichannel Orchestration: A Holistic Solution

Omnichannel orchestration goes beyond simply supporting multiple channels. It connects them into a unified intelligence layer that correlates activity across the entire customer journey, creating a continuously updated risk profile rather than isolated point-in-time assessments.

Unlike a traditional multichannel architecture, where systems exchange only limited information, omnichannel orchestration enables every customer interaction to contribute to a single, evolving view of risk. Whether a customer begins their journey through online banking, a mobile app, a contact centre, or another touchpoint, each event provides context for the next.

As a result, events that appear legitimate in isolation can be recognised as part of a coordinated attack when viewed together. This cross-channel visibility enables fraud teams to identify emerging threats, respond earlier, and make more informed decisions than would be possible in a traditional multichannel environment.

As fraud becomes increasingly sophisticated, omnichannel orchestration is no longer a competitive advantage - it's becoming a fundamental requirement for effective fraud prevention. By correlating fragmented signals into a unified risk picture, financial institutions can detect complex attacks earlier and better protect both customers and assets.

Discover how Acoru helps financial institutions detect cross-channel fraud with omnichannel orchestration. Contact our team to learn more.