Discover Network Shows How Sharing Data Can Turn the Tide on Fraud

Instead, it is a fast-moving, adaptive threat that cuts across eCommerce, digital payments, account access and identity, growing in frequency and sophistication as commerce increasingly shifts into digital and mobile channels.

What once appeared as isolated incidents now reflects systemic pressure on banks, merchants and networks, with customers feeling the effects of fraud, too.

This growth is not limited to a single payment rail or use case. From account takeovers and authorized push payment scams to synthetic identities and merchant fraud, attackers are probing every weak point in the financial ecosystem, often simultaneously.

The scale and speed of these attacks make it clear that traditional, institution-by-institution defenses are no longer sufficient.

How Fraudsters Are Using Technology Against Banks

Fraudsters are no longer relying on blunt instruments. Artificial intelligence is being used to automate attacks, test defenses and refine social-engineering scripts in real time. Deepfake audio and video have added a new layer of credibility to impersonation scams, making it harder for customers and frontline staff to distinguish legitimate requests from fraudulent ones.

Mobile devices have become a primary attack point, with smartphones sitting at the intersection of identity, payments and authentication, giving criminals a powerful channel to exploit behavioral cues, device signals and user trust. These tools allow attackers to move faster and iterate more quickly than static rule-based systems can respond.

Why Banks Cannot Fight Fraud Alone

Even the most sophisticated bank only experiences a narrow slice of fraud activity happening worldwide. Criminals do not respect institutional boundaries, and attacks rarely stay confined to a single issuer, merchant or geography. When data is siloed, warning signs often appear too late, after losses have already spread across the ecosystem.

Standalone defenses also struggle to distinguish between emerging fraud patterns and normal customer behavior. Without broader context, banks face a trade-off between stopping fraud and creating friction for legitimate users, a balance that becomes harder as fraud volumes rise.

The Case for a Consortium and Shared Signals

A consortium-based approach changes the equation. By pooling intelligence across participants, networks can identify patterns that no single institution could see on its own. Sharing data transforms isolated signals into actionable insights, allowing defenses to adapt as fraud tactics evolve.

This is where the network effect becomes a force multiplier. Each additional participant strengthens the system, improving detection accuracy while reducing false positives. As more transactions, devices and behaviors are analyzed collectively, fraudsters lose the advantage of asymmetry and speed.

Signals, Tokenization and Network-Level Defenses

Discover® Network has highlighted the growing importance of signals in fraud prevention. Device behavior, transaction context, historical patterns and real-time indicators provide a richer picture of risk when evaluated collectively rather than in isolation.

Tokenization also plays a critical role in reducing the exposure of sensitive data while preserving the ability to recognize legitimate activity. By replacing raw credentials with secure tokens, networks can limit the usefulness of stolen data while maintaining continuity across transactions and channels.

Tools such as Enhanced Decisioning from Discover Network are designed to give issuers additional transaction data to make more informed approve-or-decline decisions, improving fraud capture while reducing false declines. Other Discover Network capabilities, including near real-time Fraud Alerts, Account Incident Manager and merchant validation tools, focus on surfacing confirmed fraud signals earlier and sharing them across participants so patterns can be identified and acted on quickly.

Data as a Safeguard Against Fraud

The same data that once overwhelmed banks is increasingly being turned against fraudsters. At network scale, patterns emerge faster, correlations sharpen and defenses improve continuously. Fraud becomes easier to spot precisely because attackers reuse infrastructure, devices and behaviors across victims.

This creates an important shift in momentum. Instead of reacting to losses after the fact, banks and networks can anticipate threats, intervene earlier and continuously refine defenses as conditions change.

Stopping Attacks Earlier, Letting Commerce Flow

Shared data enables earlier intervention. By identifying shifts in behavior and emerging trends, networks can flag threats before they escalate into full-fledged attacks. That proactive posture is essential in an environment where fraud adapts quickly and spreads fast.

Just as important, stronger fraud defenses create positive ripple effects. When risk is assessed more accurately, fewer legitimate transactions are incorrectly declined. Customers experience less friction, merchants see higher authorization rates, and trust in digital commerce deepens.

“Technology has accelerated the evolution of fraud, but it has also elevated the power of data sharing,” said Ramesh Devaraj, vice president, Authentication, Fraud and Disputes Products. “When intelligence is shared at network scale, insights deepen, defenses strengthen, and trust extends across the payments ecosystem.”