Fraud network intelligence: The key to AI fraud prevention

AI isn’t just just changing how we work–it’s changing how fraud works. For bad actors, it’s become a powerful ally that allows them to churn out convincing scams faster and more efficiently than ever. 

In 2025, more than 22,000 reports of fraud involved the use of AI resulting in nearly $900M in losses. This means not only are fraudsters leveraging AI–they’re leveraging it successfully. That is because traditional fraud tools are often siloed and rely on static, point-in-time checks that can miss broader fraud signals like a group of 10 devices fraudulently creating accounts across multiple platforms. 

Taken on their own, these accounts or transactions may look legitimate. But when seen through the lens of fraud network intelligence, the red flags become much easier to see. 

What is fraud network intelligence?

Fraud network intelligence is the ability to detect fraud by analyzing how identities, devices, bank accounts, and behaviors connect across the broader financial ecosystem, rather than just within a single app or bank. 

Instead of evaluating a customer or transaction as a single event, fraud network intelligence looks for patterns over time and across networks. It can identify when the same device appears across multiple identities, when a bank account is linked to suspicious activity in a different app, or when seemingly unrelated users exhibit similarly suspicious behavior. 

This wider view matters because modern fraudsters often reuse devices, accounts, phone numbers, or synthetic identities across multiple institutions and platforms. Traditional fraud tools that rely on static identity attributes, rules engines, or one-time checks can miss these less accessible signals.

How AI is changing the fraud threat landscape

AI has fundamentally changed financial fraud. What once required technical expertise and coordinated effort can now be automated with AI tools that are widely available to the public. This means sophisticated attacks can be launched and scaled faster than ever before. 

That shift is showing up across nearly every major type of fraud: 

• Synthetic identity fraud at scale: AI can generate convincing synthetic identities in seconds by combining stolen personal information with fabricated details, making fake applicants harder to distinguish from legitimate customers.

• Deepfakes and biometric spoofing: AI-generated images, video, and voice can bypass the “eye test” and trick traditional identity verification controls. 

• Automated account takeover: AI agents can test thousands of stolen credentials in seconds, quickly adapt to failed login attempts, and probe authentication workflows for weaknesses they can exploit. 

• Coordinated fraud rings: AI makes it easier to launch distributed attacks across multiple institutions, accounts, and devices while keeping activity below the thresholds many rules-based systems rely on.

The result is a fraud environment where attacks are faster, cheaper, and harder for traditional fraud strategies to spot.

Why traditional fraud risk strategies fall short

Most traditional fraud systems were built around static rules, isolated data, and point-in-time decisions. That approach worked in the past, but now the limitations are becoming more obvious:  

Rules-based systems can’t adapt

Rules engines rely on fixed thresholds and predefined patterns—such as flags for specific locations, device type, or transaction size. The problem is that fraudsters quickly learn how those thresholds work and change their approach to avoid triggering them. With AI, the probing cycle becomes even faster, often allowing attackers to refine their approach in seconds. 

Limited visibility into the broader ecosystem

Most institutions only see activity within their own environment. That creates blind spots when fraud is coordinated across multiple platforms, accounts, and institutions. For example, an account on one trading app might appear legitimate while actively participating in a fraud on another. 

Over-reliance on point-in-time checks

Many fraud decisions are still anchored in onboarding steps like identity verification and document checks, but fraud can happen at any stage of the customer relationship. Synthetic identities, account takeovers, and bust-out fraud can all pass initial checks–only to start their fraud attempts later.  

These limitations mean if your fraud intelligence only reflects what happens in your own four walls, you're always one step behind the fraudsters.

What an effective AI fraud prevention system needs

As fraud becomes more adaptive, organizations need more than single point-in-time checks. Effective fraud detection requires a layered risk strategy with several core capabilities: 

Real-time, lifecycle-wide risk scoring

Effective systems continuously evaluate risk across the entire user journey from account creation to login, payments, and account changes. This allows institutions to detect changes in user behavior as they happen and catch fraud before funds are moved.  

Cross-network intelligence signals

Fraud signals are easier to spot across a broader ecosystem. For example, one organization on its own is unlikely to know an account on their network is linked to multiple identities or suspicious behaviors that repeat across different apps and services. Cross-network visibility can surface patterns that a single organization won’t see, but the value of those signals depends on the breadth of the network. 

Networks that span financial institutions, fintech apps, digital wallets, cryptocurrency platforms, property technology companies, and other financial services can connect these otherwise isolated signals, creating a more complete picture of risk.

Convergence of identity, device, behavior, and account data

Fraudsters exploit gaps between signal types. A stolen identity might look legitimate on paper, while a compromised device or unusual behavior tells a different story. Effective fraud systems don't just evaluate these signals independently—they evaluate them together.

Some systems aggregate identity, device, account, and behavioral data into a single risk score. More advanced approaches also analyze the relationships between those signals, using network graphs to find hidden connections between users, devices, accounts, and applications. This broader context can highlight coordinated fraud activity that would be difficult to detect when reviewing individual accounts in isolation.

Fast feedback loops through consortium signals

Fraud moves quickly, so detection systems need to learn quickly, too. Fraud consortiums, where multiple companies and financial institutions share information about fraud attempts, allow stronger feedback loops across organizations. Consortium-based signals allow fraud insights—like confirmed account takeovers or known third party fraudsters—to spread across networks in near real time, helping stop emerging threats before they scale. 

Not every institution will use these capabilities in the same way. Some may treat network signals as an additional risk data layered on top of existing rules engines. Others with more advanced data science teams may incorporate that data into proprietary models for more customized scoring. The key is flexibility that allows each organization to build a risk model that fits their needs.

How Plaid Protect puts network intelligence to work

Plaid Protect is an AI-enhanced fraud intelligence platform that helps detect fraud using real financial behavior across users, devices, and bank accounts—rather than static identity data or isolated device checks.

At its core is a dynamic Trust Index that evaluates risk of fraud in real time based on activity across millions of interconnected signals, including: 

• Activity across 7,000+ apps and services

• Device interactions across billions of sessions

• Bank account patterns, including changes in spending patterns 

• Identity verification and consistency signals

• Consortium and account takeover (ATO) intelligence shared across the network

Most fraud systems rely on static attributes or single-session behavior. Plaid Protect instead builds context from real financial activity across 500M+ linked devices. This means it can surface signals when a device appears to be opening accounts on multiple platforms at a rapid pace, helping your team connect otherwise unrelated identities or flag inconsistent IP addresses.

That scale makes it possible to detect relationships that don’t exist within any one institution’s data—turning fragmented signals into a connected view of risk. Early testing shows that Plaid Protect could help surface fraud signals that may otherwise have gone undetected:

• 40% of first-party fraud could have been caught for a public lender at a 5% step-up rate

• 47% of fraud could have been caught for a large crypto firm by stepping up 1 in 10 users

• 50% of losses could have been prevented for a cash advance app at a 5% step-up rate

One million connections are made through Plaid every day. Those connections span thousands of financial institutions, fintechs, digital wallets, lending platforms, investment apps, and other financial services. As a result, Plaid helps businesses surface fraud signals across a broad range of consumer financial activity. 

→ Learn more about how Plaid Protect can help you detect and respond to fraud signals without introducing friction.