Behavioral AI built to stop compromised email, ERP, and vendor accounts from turning into costly B2B payment fraud.
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Account Takeover (ATO) occurs when attackers gain access to trusted business accounts, like email, ERP, banking, or vendor platforms, and use them to impersonate legitimate users, manipulate business processes, or steal information, fueling some of the most common and costly forms of payment fraud today.
With projected account takeover losses expected to reach $17B in 2025, these attacks are becoming faster, more convincing, and harder to detect.
AI-driven phishing, stolen credentials, and social engineering now allow attackers to operate inside legitimate business accounts using trusted access and normal workflows. In many cases, the activity looks completely normal.
At the same time, many financial systems, including ERP, vendor onboarding, and payment platforms, lack the behavioral context needed to detect these sophisticated account takeover attacks. That leaves organizations responsible for moving millions of dollars increasingly exposed.
Many legacy ATO tools focus on login risk, looking for suspicious devices, impossible travel, unusual IPs, or failed authentication attempts. But once access is verified, visibility often drops.
Attackers can then operate through trusted accounts, approved sessions, and legitimate workflows without triggering traditional alerts.
As fraud increasingly happens after login, organizations need more than authentication signals alone to detect malicious behavior.
Attackers now use AI-driven phishing and credential stuffing to test massive volumes of stolen credentials. Combined with how cheap compromised credentials have become, it creates enormous noise for traditional ATO systems.
It’s becoming increasingly difficult to distinguish legitimate users from fraudsters based on login activity alone, especially without real-time fraud detection across every touchpoint.
ERP, vendor, and payment systems were not designed to detect sophisticated account takeover activity, even though they control the company’s money.
Once attackers gain access to a legitimate account, their activity often appears normal inside financial workflows. Even organizations with mature identity and email security can struggle to detect these attacks without behavioral visibility across systems.
That creates a major blind spot for organizations responsible for approving, processing, and moving millions of dollars.
Most organizations still manage security, identity, ERP, and payments in separate systems that rarely communicate with each other. Attackers exploit these gaps, operating through trusted accounts and financial workflows without triggering immediate red flags.
By the time suspicious activity is connected across systems, financial damage may have already occurred.
After a phishing-led account takeover caused $2.2M in losses, the company turned to Trustmi to identify fraud hidden inside trusted financial workflows
“They were living in our accounting system and watching our behaviors—and we didn’t know they were in there. We wanted something that was going to understand our everyday behavior and how we operate.”
Businesses increasingly rely on connected financial systems, vendor platforms, and digital workflows to move money quickly. When attackers gain access to legitimate business accounts, they can operate inside those trusted systems to steal sensitive information, manipulate payments, or commit fraud without immediately raising suspicion.
As AI-driven phishing, stolen credentials, and social engineering make these attacks easier to execute at scale, projected ATO-related losses are expected to reach $17 billion in 2025.
Many traditional ATO tools focus primarily on authentication signals like suspicious logins, devices, or IP addresses. But modern fraud often happens after authentication, inside legitimate accounts and trusted workflows. Trustmi analyzes behavioral activity across financial systems, payment workflows, email, ERP, and vendor environments to identify suspicious activity traditional controls may overlook.
Trustmi uses Adaptive Behavioral AI to continuously analyze patterns of account activity, payment behavior, transaction history, and system interactions in real time. By identifying unusual behavior across connected systems, Trustmi helps organizations detect suspicious activity and payment fraud hidden inside legitimate workflows.
Many traditional ATO detection methods rely on static rules, manual reviews, or isolated authentication signals. Trustmi’s Adaptive Behavioral AI continuously learns how users, systems, and financial workflows normally behave, helping organizations identify subtle behavioral changes tied to compromised account activity.
Yes. While email is a common entry point, account takeover can impact ERP systems, payment platforms, vendor portals, procurement systems, and identity platforms. Attackers increasingly target the systems involved in financial workflows because legitimate account access can be used to manipulate payments or hide suspicious activity.
"Trustmi provided transparency into our payment process to see where cyberattacks and errors were happening and full protection without changing our workflow."
"Like many businesses today, we've experienced cyber attacks on our payment process, but we didn't realize the extent to which we were at risk until we evaluated Trustmi. Now we're confident we'll be able to avoid future attacks with their platform."
"Trustmi's platform is an important tool for our team. Their Payment Flows module increases our payment cycle security, and our team has also managed to cut down the time for preparing payments reports from half a day to half an hour."
Protecting businesses globally against socially engineered fraud and errors.
Zero Compromise
Stops fraud without disrupting
legitimate payments.
Protecting businesses globally against socially engineered fraud and errors.
By Eliminating Fraud and Payment Errors
Manual Process Time Reduced