How layered intelligence helps financial institutions identify check fraud sooner and make more informed decisions.

October is Cybersecurity Awareness Month and we’re committed to helping financial institutions strengthen cybersecurity and build more secure, resilient digital banking experiences. Throughout the month, we’ll be sharing insights, resources and perspectives to support the ongoing work of staying ahead of evolving threats.

Despite the continued shift toward digital banking and electronic payments, check fraud remains a persistent challenge. The 2026 Federal Reserve Financial Services Risk Officer Report found that 63% of financial institutions experienced check fraud attempts in the last 12 months.

Businesses face similar pressures. According to the 2026 AFP Payments Fraud and Control Survey, checks were most frequently affected by fraud in 2025, with 58% of organizations reporting check fraud. Payment risk is not going away; 72% of organizations that use checks expect to continue doing so for the foreseeable future.

For financial institutions, the challenge is determining whether they have enough intelligence early enough in the process to distinguish meaningful risk from legitimate activity and make a more informed decision. Doing so requires more context than any single fraud control can provide.

Why check fraud detection requires connected intelligence

Check fraud can take many forms, including counterfeit items, altered checks, payee forgery, duplicate deposits, and manipulated or recaptured check images.

At the same time, fraud does not necessarily stay within one channel or one financial institution.

The same suspicious check, compromised account, or fraud pattern may appear across the financial system. But individual financial institutions have limited visibility into activity occurring outside their own environments. That creates an intelligence gap.

Traditional controls such as Check Positive Pay, transaction monitoring, business rules, manual review, and employee training remain important. However, as check fraud evolves, financial institutions are adding new layers of intelligence, including artificial intelligence (AI)-powered image analysis and broader network data.

The Federal Reserve’s 2026 research reflects this shift, identifying AI image analysis among the approaches financial institutions are using to detect alterations and anomalies and strengthen existing fraud controls. Rather than replacing existing controls, layering in additional context helps financial institutions identify risk and make more informed decisions.

Layered intelligence can improve decision quality

An effective check fraud strategy increasingly depends on bringing together multiple forms of intelligence:

• Transaction data can provide context around account activity.
• Image analysis can help identify anomalies or inconsistencies within the check itself.
• Business rules can reflect an institution’s specific risk policies.
• Cross-institutional intelligence can provide visibility into checks, accounts, or fraud patterns that may have appeared elsewhere.

Each layer answers a different question. Together, they give fraud teams a more complete picture of potential risk than any individual control can provide on its own.

How consortium intelligence expands visibility

Consortium intelligence expands a financial institution’s visibility beyond its own environment by revealing whether a check, account, or fraud pattern has appeared at another financial institution. It adds context to what the financial institution already knows about its customers or members and transactions.

For financial institutions evaluating consortium intelligence, useful questions include:

• How broad is the underlying network?
• What types of fraud intelligence are available?
• How quickly can new intelligence inform decisions?
• Can that intelligence be incorporated into existing workflows?
• Does it give analysts useful context about why an item may represent greater risk?

The value ultimately depends less on the volume of data available than on whether that data helps improve fraud decisions.

One example of this layered approach is Mitek’s Check Fraud Defender, which combines front- and back-of-check image analysis, business rules, and cross-institutional consortium intelligence to give fraud teams a broader view of potential risk.

Bringing real-time check fraud detection into the deposit experience

Timing is everything. A suspicious item discovered after a transaction has moved farther downstream can create a very different operational challenge than one identified closer to the point of deposit.

That is why financial institutions are increasingly looking at how fraud intelligence can move earlier in the transaction lifecycle.

Through Alkami’s partnership with Mitek, financial institutions can use Check Fraud Defender to analyze check images from existing processing environments with suspicious items presented to fraud teams for investigation.

Identifying meaningful risk before funds are released gives an institution more flexibility to respond, including allowing lower-risk deposits to proceed, routing suspicious items for review, applying a hold or taking other action based on its risk policies. These decisions can affect potential fraud losses, operational workload, investigation requirements and the handling of legitimate deposits.

How better check fraud detection supports the account holder experience

Stronger fraud protection should not cause more friction for actual customers and members.

When a financial institution lacks confidence in its ability to distinguish suspicious activity from legitimate transactions, it may rely more heavily on manual reviews, holds, or other interventions.

More precise risk intelligence can help financial institutions focus reviews on deposits with stronger risk signals while allowing lower-risk deposits to efficiently move forward with fewer delays.

Striking the right balance reinforces trust with consumers who expect their financial institution to protect them. A 2026 Morning Consult survey conducted on behalf of the American Bankers Association found that 45% of U.S. adults selected banks as the organization they trusted most to protect them from fraud, compared with 9% for non-bank fintech providers. Among bank customers, 86% said their bank takes proactive steps to protect them from fraud and scams.

Therefore, fraud strategy has implications beyond loss prevention. It can also influence the trust and experience surrounding everyday banking interactions.

Measure decision quality, not alert volume

As fraud technology evolves, financial institutions should also reconsider how success is measured.

Alert volume alone says little about whether a fraud strategy is effective. More meaningful performance metrics include fraud losses prevented, fraud identification or capture rates, false-positive rates, investigation time, manual review volume, analyst productivity, and unnecessary holds on legitimate transactions.

These metrics help financial institutions evaluate whether fraud controls are identifying meaningful risk efficiently.

For example, a system that produces fewer but more relevant alerts may provide greater operational value than one that sends a larger volume of transactions to manual review.

Likewise, reducing review time matters most when analysts can see visual evidence of discrepancies, risk insights from image analysis and consortium intelligence, and related account or fraud pattern information in one place. With that context, they can prioritize higher risk items for investigation and decide whether to let the deposit proceed, apply a hold or take additional action.

Building a more connected check fraud strategy

Check fraud prevention has evolved to focus on improving how fraud intelligence is connected, interpreted, and acted on.

For financial institution leaders, the priority should be understanding where gaps still exist across the deposit lifecycle. Evaluate the following:

• Are teams seeing suspicious activity early enough?
• Do analysts have the context they need to make confident decisions?
• Are fraud controls working together in a way that protects legitimate account holders without creating unnecessary friction?
• Are fraud detection tasks automated where appropriate, while preserving human oversight for higher-risk activity?

As fraud continues to move across channels and institutions, the organizations best prepared to respond will be those that can turn multiple sources of intelligence into faster, more informed decisions.

Learn how Alkami and Mitek can help your financial institution bring advanced check fraud intelligence into the deposit journey here. You can also explore Alkami’s layered security approach by registering for our upcoming webinar, Layered Security in Action: Protecting Every Step of the Digital Banking Journey, register here.

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