How separating genuine intent from fraudulent behavior stops account takeover in the digital banking platform without slowing down real account holders.

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.

Why Credential Checks No Longer Stop Account Takeover

Most fraud teams have approved a fraudulent transaction and blocked a legitimate one on the same day, even though the credentials matched, the device was familiar, and the login checks passed in both cases. Authentication confirmed identity correctly each time; it never evaluated what the person intended to do once inside.

For decades, account takeover (ATO) was treated primarily as an authentication failure. Financial institutions responded by layering on multi-factor authentication, one-time passcodes, and step-up challenges. Those controls remain important, especially when criminals use stolen credentials from unfamiliar devices. They are less effective when attackers use valid credentials and recognized devices or when a genuine account holder is manipulated into authorizing a fraudulent transaction.

According to Javelin, account takeover, which makes up a part of both existing card fraud and non-card fraud, saw an 18% increase in victims from 5.1 million in 2024 to 6 million in 2025 — and reports filed with the Financial Crimes Enforcement Network (FinCEN) increased more than 36% year over year (Federal Reserve Financial Services, February 2026).

What Makes Modern Account Takeover Fraud Difficult to Detect

Three users can enter identical credentials from the same recognized device and follow the same sequence of actions: one from a genuine account holder, one from a fraudster who has purchased those credentials, and one from a genuine account holder acting under a scammer’s instruction. To a fraud system focused primarily on credentials, devices, and transaction history, these sessions may look nearly identical.

That is because most fraud systems rely heavily on historical data, including:

• Transaction values
• Device identifiers
• IP addresses
• Geolocation
• Past account activity

This information shows where an account has already been, not where it is headed, and it typically lives in a separate transaction monitoring system that only evaluates the account after a payment has been submitted. Sophisticated attackers use legitimate credentials, familiar devices, and expected transaction patterns specifically because those patterns clear historical checks. Detection lags as a result, models stay reactive, and money often leaves the account before the fraud is confirmed.

How Behavioral Intelligence Predicts Risk Instead of Reacting to It

Behavioral intelligence adds context by evaluating how a person moves through a banking session — navigation speed, hesitation at decision points, typing rhythm — while the session is still open, rather than only confirming identity at the door. Because that behavior changes before a fraudulent transfer completes, financial institutions can flag the session while there is still time to intervene.

In practice, that means tracking four things throughout the session:

• How the user navigates through the session
• How they interact with menus and prompts
• How they input and edit data
• How they respond when a screen introduces friction or an unexpected step

A fraudster working from a purchased credential list often moves in an overly linear path, since the navigation was scripted rather than learned through repeated use of the account. A remote-access scammer directing a victim in real time produces a different pattern. The genuine account holder hesitates at each instruction, enters data more slowly than their own history would predict, and sometimes pauses mid-field as if waiting for direction. Both patterns are visible before the transaction completes, which is what allows the institution to intervene before the loss occurs rather than investigate after it.

Within Alkami’s Digital Banking Platform, this evaluation can run through BioCatch’s Account Takeover Protection, which adds a behavioral layer to the identity and device checks already running in the digital banking platform rather than replacing them. BioCatch’s Mule Account Detection applies the same behavioral approach to a different problem. Accounts used to move stolen funds tend to show fragmented, inconsistent session patterns across multiple logins, which that solution is built to surface before a suspicious activity report would otherwise catch them.

What Results Look Like When Financial Institutions Deploy Behavioral Intelligence

Through Alkami’s partnership with BioCatch, regional banks and credit unions using behavioral fraud prevention tools stopped more than $263 million in fraud in 2025. At the institution level, that translates into concrete outcomes – earlier detection, fewer false positives, and fraud stopped before funds leave the financial institution.

At ORNL Federal Credit Union, a Tennessee-based credit union serving more than 225,000 members, behavioral intelligence flagged account takeover attempts that had already passed multi-factor authentication, based on session behavior that did not match the member’s established pattern. The credit union used that detection to intervene before funds moved, saving members more than $1 million in fraud losses in just six months.

To learn how behavioral intelligence can strengthen the fraud prevention strategy for your financial institution, learn more 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.