2026 fraud trends: AI volume, synthetic IDs, post-origination shifts

Artificial intelligence is changing fraud in a very distinct way: In addition to making attacks more sophisticated, AI is making them far more frequent.

That was a central takeaway from a recent episode of The Good Question Podcast, where Ronan Burke, CEO and co-founder of Inscribe, and Laura Speakman, co-founder and president of Alloy, sat down to unpack insights from their newly released annual fraud reports. While Inscribe’s research focuses on document fraud and Alloy’s looks at the broader fraud landscape, the conclusions converged quickly.

As financial institutions head into 2026, fraud is becoming louder, faster, and more operationally expensive — and that reality is forcing a rethink of how fraud prevention works.

Below is a reporter-style recap of the key fraud trends Ronan and Laura discussed, and what they signal for fraud leaders planning ahead.

AI-driven fraud is scaling volume before sophistication

When AI comes up in fraud conversations, the focus is often on advanced deepfakes or perfectly fabricated identities. But Laura pointed to a more immediate impact: sheer volume.

AI tools make it easier to generate large numbers of fraud attempts quickly. Even when those attempts are sloppy or easily detectable, they still demand attention. They trigger reviews, consume analyst time, and create friction for legitimate customers.

From Inscribe’s document fraud lens, Ronan added an important nuance. While fully AI-generated documents are often easy to spot, AI is proving far more effective when used to modify real documents — changing names, dates, or financial figures just enough to overwhelm brittle controls.

The result is not just more fraud, but more noise.

2026 takeaway: Fraud teams should plan for AI-driven attack volume as a constant, not an exception.

Fraud detection is shifting beyond onboarding

One of the more striking findings from Alloy’s research is a measurable decline in fraud being detected at onboarding, paired with an increase in fraud detected after transactions occur.

This shift does not necessarily mean onboarding controls are improving. Instead, Laura suggested it reflects how modern fraud is becoming harder to classify at the point of entry. Many accounts that appear legitimate initially later reveal signs of fraud through behavior, transactions, or account changes.

Scams, money mule activity, and hybrid fraud models blur traditional categories like first-party and third-party fraud. As Ronan noted, some applicants may look legitimate because they are real people—until external manipulation or document alteration enters the picture.

2026 takeaway: Fraud prevention can no longer stop at onboarding. Continuous, post-origination monitoring is becoming essential.

Synthetic identity fraud is harder to define — and harder to catch

Synthetic identity fraud remains a top concern, but the podcast made clear that the category itself is becoming increasingly ambiguous.

Rather than fully fabricated identities, many cases now involve hybrids: real names paired with fake addresses, altered documents, or AI-assisted guidance on how to submit a convincing application.

Ronan highlighted a growing pattern in customer data: large language models are being used not only to create content, but to coach fraudsters step-by-step on what information to provide and how to behave once an account is opened.

Laura added that as fraud becomes more convincing, organizations may misclassify sophisticated third-party fraud as first-party behavior—because it looks indistinguishable from a real customer until losses occur.

2026 takeaway: Synthetic identity should be treated as a spectrum, not a checkbox. Detection strategies must account for partial truth and mixed signals.

Actionable AI matters more than detection alone

Another consistent theme was that detection, by itself, is not enough.

Laura described a common pitfall: organizations continuously buying new models without the ability to orchestrate them into a coherent decision or respond in real time. Fraud prevention fails if teams can identify risk but have no practical way to act without shutting down growth channels.

Instead, both speakers emphasized the importance of actionable AI — systems that not only detect risk, but trigger proportionate responses such as step-up verification, added friction, or targeted review.

The goal is not to stop all activity, but to route the right cases to the right level of scrutiny.

2026 takeaway: Fraud programs need detection and response, tightly integrated.

Fraud prevention is increasingly viewed as a growth lever

One of the most counterintuitive insights from the conversation was this: aiming for zero fraud is often a mistake.

Laura argued that overly aggressive controls can block legitimate customers, increase call-center volume, and quietly suppress growth. Measuring only fraud dollars stopped misses the hidden cost of false positives and unnecessary friction.

Instead, mature fraud teams are beginning to ask a different question: how much good business did we accidentally decline?

Ronan reinforced this operationally, noting that a small percentage of edge cases often consumes the majority of review time. Reducing false positives can have outsized impact on efficiency and customer experience.

2026 takeaway: Fraud metrics must balance prevention with approval, conversion, and operational cost.

Credit unions and omnichannel fraud are emerging pressure points

Alloy’s data shows credit unions reporting some of the sharpest increases in fraud events. While the exact cause is unclear, Laura suggested a likely pattern: fraud migrates toward weaker defenses.

As institutions invest heavily in digital channels, attackers may shift to branches, contact centers, and account servicing flows. The rise of omnichannel banking—starting online and finishing in person — creates new gaps if fraud prevention does not follow the customer journey.

Ronan added that fraudsters optimize for efficiency. Institutions that lag in adopting modern controls may face disproportionate pressure.

2026 takeaway: Omnichannel fraud prevention is becoming mandatory, not optional.

The defining theme for 2026: layered, lifecycle fraud strategies

Across identity, documents, transactions, and behavior, one message was consistent: single-threaded fraud controls will not hold up in an AI-driven environment.

Ronan and Laura both pointed to the need for layered defenses that span the full customer lifecycle — combining identity intelligence, document signals, behavioral monitoring, and adaptive responses.

As AI continues to industrialize fraud, success in 2026 will depend less on any single model and more on how well teams integrate signals, test decisions, and adapt over time.

For fraud leaders, the mandate is clear: build systems that can absorb volume, reduce noise, and protect trust — without slowing the business down.

Frequently Asked Questions: 2026 Fraud Trends

What are the biggest fraud trends for 2026?

The biggest fraud trends heading into 2026 include a sharp increase in AI-driven fraud attempts, the rise of hybrid and synthetic identities, a shift from onboarding fraud to post-origination fraud, and growing pressure on omnichannel fraud defenses. Rather than isolated attacks, fraud is becoming higher-volume and more operationally disruptive, forcing teams to rethink how they detect and respond to risk across the full customer lifecycle.

How is AI changing fraud in financial services?

AI is changing fraud primarily by increasing scale and speed. Fraudsters are using AI tools to generate, modify, and test fraudulent applications, documents, and identities at unprecedented volume. Even low-quality AI-generated fraud creates operational burden by triggering reviews and false positives. At the same time, AI is also being adopted by fraud teams to automate low-risk reviews and reduce manual workload.

What is post-origination fraud and why is it increasing?

Post-origination fraud refers to fraudulent activity that occurs after an account is opened or a customer is approved, such as suspicious transactions, account takeovers, or misuse of legitimate accounts. It is increasing because modern fraud often bypasses onboarding controls and reveals itself later through behavior. As a result, fraud detection is shifting toward continuous monitoring rather than one-time checks.

Why is synthetic identity fraud so difficult to detect?

Synthetic identity fraud is difficult to detect because it often blends real and fake information. Many modern cases involve hybrid identities where some attributes are legitimate while others are fabricated or manipulated. AI tools further complicate detection by helping fraudsters optimize applications and behavior, making synthetic identities harder to distinguish from real customers.

How does document fraud fit into broader fraud trends?

Document fraud is increasingly used as part of larger fraud schemes rather than as a standalone tactic. AI has made it easier to modify real documents, such as bank statements or utility bills, in subtle ways that evade basic checks. Because documents often appear trustworthy, they must be evaluated alongside identity, behavioral, and transactional signals rather than in isolation.

What does “actionable AI” mean in fraud prevention?

Actionable AI refers to fraud systems that not only detect risk but also trigger appropriate responses. This can include step-up verification, additional documentation requests, or targeted reviews. Detection without a clear response path can slow operations or block legitimate customers, so actionable AI focuses on enabling fast, proportionate decisions.

Is fraud prevention becoming a growth strategy?

Yes. Fraud prevention is increasingly viewed as a growth enabler rather than just a defensive function. When done well, it reduces friction for legitimate customers, improves conversion rates, and lowers operational costs. Many fraud leaders are now measuring not only fraud prevented, but also the amount of good business unintentionally blocked by overly strict controls.

Why are credit unions experiencing higher fraud rates?

Credit unions are reporting higher fraud rates in part because fraud tends to move toward weaker or less-protected channels. As digital defenses improve, fraud may shift to branches, contact centers, or account servicing workflows. Limited budgets and rapid digital transformation can also make it harder for credit unions to maintain consistent fraud controls across channels.

What is omnichannel fraud prevention?

Omnichannel fraud prevention means applying consistent fraud detection and response across all customer touchpoints, including online, mobile, branch, ATM, and call center interactions. As customers move seamlessly between channels, fraud prevention strategies must follow the same journey to prevent attackers from exploiting gaps.

How should fraud teams prepare for 2026?

Fraud teams should prepare by adopting layered, lifecycle-based strategies that combine identity, document, behavioral, and transaction signals. Investing in systems that reduce false positives, support continuous monitoring, and enable fast responses will be critical as AI-driven fraud continues to scale.