A Guide to Account Takeover (ATO) Fraud Prevention & Detection

The Feedzai Fraud Readiness Index 2026 ranks account takeover fraud (ATO) among the top three most costly types of fraud by financial leaders. Data from Javelin Strategy & Research supports this, noting that ATO fraud resulted in $15.6 billion in losses for US adults in 2025.¹

ATO fraud has surged as criminals use impersonation, phishing, and SIM-swapping tactics to access accounts or convince account holders to share credentials and authentication codes. Read on to learn how account takeover fraud works and get essential tips to protect your customers.

Key Takeaways

• The Feedzai Fraud Readiness Index 2026 ranked ATO scams as the third most costly type of fraud, according to 49.5% of global financial leaders.
• Data from Javelin Strategy & Research finds account takeover fraud resulted in $15.6 billion in losses for US adults in 2025.¹ 
• ATO occurs when fraudsters illegitimately access accounts through credential stuffing, malware, SIM swapping, and more.
• Several avenues can help prevent ATO attempts, including behavioral biometrics, malware detection, and device fingerprinting.
• Feedzai’s Digital Trust solution leverages AI-driven behavioral biometrics and real-time risk scoring to block unauthorized access, protecting revenue without disrupting customer experiences.

What is Account Takeover Fraud?

Account takeover fraud is a type of cybercrime where unauthorized individuals access and control someone else’s online accounts. They typically intend to steal funds or personal information. ATO fraud can affect businesses and individuals alike, leading to financial loss and reputational damage.

The Feedzai Fraud Readiness Index 2026 ranked ATO fraud as the third most costly type of fraud reported by global financial leaders, followed by APP scams and synthetic identity fraud.

“Continuous adaptive trust treats identity as an ongoing probability calculation, rather than a point-in-time verdict. Without it, fraud prevention risks becoming a reactive practice that draws on past trends, rather than a proactive safeguard that anticipates new threats. ” — Dan Holmes, VP of Global Product Planning & Strategy, Feedzai

How Does Account Takeover Fraud Work?

Account takeover can be completed in three key steps. 

1. Fraudster Gains Unauthorized Access to Legitimate Accounts

ATO attacks often result from other fraudulent activities, including phishing, malware, social engineering, or data breaches. These tactics can leave user credentials and personally identifiable information (PII) vulnerable to compromise by bad actors. Once they access this information or learn key details by reviewing a target’s social media profile, they can unlawfully access bank accounts.

2. Makes Small, Often Unnoticed Changes 

Once they have access to the account, fraudsters start with small, non-monetary actions. They might update or alter PII information. Or they might add a new user, change the account password, or request a new payment card.

Because these activities are non-monetary and considered regular activity, they are harder to detect.

3. Take Control of a Breached Account

If the fraudster changes the account details, they can use the account to make purchases or transfers. They may also find reward points that they can use to buy goods.

Fraudsters can also get more personal information from the account. This includes past addresses and phone numbers. They can use this information to break into other accounts the victim has.

Types of Account Takeover Fraud

ATO attacks utilize a wide range of methods. The most common ATO methods include:

1. Credential Stuffing 

Fraudsters can obtain large volumes of sensitive information (e.g., usernames and passwords) from data breaches or purchase them on the dark web. Armed with this data, fraudsters can automate login attempts across multiple websites at scale. Credential stuffing attacks are highly effective because passwords are so commonly reused across numerous websites and accounts.

2. SIM Swapping

Fraudsters use SIM swapping to transfer a victim’s phone number to a new SIM card. By examining a person’s social media accounts, the fraudster gains personal details about their target. Next, they contact that person’s mobile carrier and switch the phone number to a new device under false pretenses. With the victim’s phone number now active on a device they control, fraudsters can bypass standard security measures like two-factor authentication (2FA) and obtain codes meant for the legitimate account holder.

3. Phishing Attacks

Fraudsters use phishing methods (including fake emails, text messages, or voicemails) to deceive users into revealing sensitive information or installing malware. Emails or text messages often contain malicious links to fake login pages that deceive victims into filling out their information.

4. Malware 

Cybercriminals can launch malware attacks targeting users’ devices. If a victim unknowingly installs malware from suspicious software or dodgy gaming apps, criminals can use programs like keyloggers to track keystrokes that reveal login information and typing patterns. Using this information, fraudsters can determine login credentials and log in to different accounts that the victim controls.

5. Man-in-the-Middle Attacks

Man-in-the-middle attacks are a devious form of digital eavesdropping. Fraudsters intercept and manipulate the communication between two unsuspecting parties. These cybercriminals masquerade as trusted entities in the conversation, fooling their targets into believing they are engaging with legitimate counterparts.

What Organizations are Impacted by Account Takeover Attacks?

Account takeover attacks are not limited to one industry. Criminals use ATO fraud to breach accounts in different business categories to perform various types of identity hijacking.

Industries Most Often Impacted by Account Takeover Attacks

Criminals commit account takeover fraud to hijack identities across various business sectors, not just one industry. Some of the most common targets include social media platforms, banks and financial institutions, email providers, and eCommerce companies.

• Banks & Financial Institutions: Financial institutions are still the top target for account takeover attacks. Criminals use compromised banking credentials, usernames, and passwords to access a legitimate customer’s account, change account information, and transfer money to an account the criminal controls.
• Fintech & Digital Wealth Providers: Fintechs, digital wallets, and peer-to-peer (P2P) lending platforms highlight seamless onboarding and faster payments as key offerings. But they also appeal to fraudsters because they attract consumers to make large deposits. While more friction is a natural customer expectation when moving larger sums, the bigger the prize, the more attractive the target.
• eCommerce Platforms: Fraudsters don’t always target financial accounts. They also target online retailer accounts on popular shopping sites. If they breach these eCommerce sites, they can deplete customer loyalty points, use stored credit cards to buy gift cards, or buy expensive items, leaving the real account holder to face the bill.
• Social Media Platforms: Fraudsters breach social media accounts to impersonate users, spread phishing links to their contacts, and harvest personal data.
• Email Providers: By hijacking an email account, attackers gain a central hub to reset passwords across multiple business or personal accounts, widening their attack.

The Cost of ATO Fraud on Businesses

Direct accounting losses tell only part of the story. Losses from ATO attacks often ripple far beyond the immediate fraud payout. When you factor in operational overhead, legal exposure, and the inevitable erosion of customer trust, the true cost to an organization multiplies rapidly.

• Direct Financial Loss: Javelin Strategy & Research finds US consumers lost $15.6 billion to ATO fraud. FBI Internet Crime Complaint Center (IC3) also reports more than 5,000 complaints related to account takeover fraud.²
• Customer Churn & Reputational Erosion: ATO can also seriously damage customer trust. Data from Visa and Javelin finds that 42% of ATO victims closed accounts with the institution where their attack occurred.³ 
• Legal & Compliance Exposure: ATO incidents that expose security failures can trigger PCI DSS payment-brand penalties, GDPR fines, or CCPA consumer lawsuits, where applicable. 

Signs to Detect Account Takeover Fraud 

Successful account takeovers can drain funds, damage credit, and erode customer trust. By monitoring these warning signs, banks can better protect their customers.

1. Sudden Account Changes or Activities. A spike in large-value transactions or withdrawals, and rapid changes to passwords, addresses, emails, phone numbers, or payment beneficiaries, are red flags that an account has been compromised.
2. Numerous Unsuccessful Login Attempts. A series of blocked logins indicates that a fraudster may be attempting to breach an account. Financial institutions should pay close attention if login attempts occur at an unusual time of day for the user or from an unfamiliar geographic location.
3. Unrecognized Devices. Login attempts from unfamiliar or unregistered devices are a red flag that a user’s credentials have been compromised.

How to Prevent Account Takeover Fraud

Now that we’ve explored how account takeover fraud happens and its impact, let’s look at how to spot and stop it. The most effective strategies to detect and prevent account takeover attacks combine the following tactics.

Multi-factor Authentication

One of the most widely used ATO fraud prevention techniques is multi-factor authentication (MFA). MFA adds extra security to the login process, requiring different types of identification to gain access. This could include something they know (like a password), something they have (like a security token or phone), or something they are (like biometric data).

Risk-based Authentication (RBA)

With RBA, you don’t just check the password; you also check where and how logins happen. Suppose something feels unusual (e.g., a login from a new device or location); a simple extra verification step can make all the difference. If a login attempt is flagged as high-risk, the system requires additional authentication steps or denies access.

Real-time Monitoring

Real-time monitoring is also critical to ATO fraud prevention. By continuously monitoring user activity, businesses can quickly detect suspicious behavior and take action to prevent fraud. Behavioral analytics and machine learning are powerful tools that can help identify anomalous behavior patterns and detect potential ATO fraud attempts.

Biometric Authentication

Biometric data, such as eye scans, facial recognition, or fingerprints, is unique to each person and impossible to duplicate. By using biometric authentication, banks can accurately verify their customers’ identity. This makes it much more difficult for fraudsters to take over accounts.

Anomaly Detection with Behavioral Biometrics

Anomaly detection with behavioral biometrics is one of the most powerful techniques for detecting ATO fraud. Behavioral biometrics analyze user behavior patterns, such as typing speed, mouse movements, and other unique behavioral characteristics.

By examining these patterns, banks can pinpoint anomalies that might indicate fraudulent activity. This may include incidents like unusual login times or unfamiliar devices. Most importantly, it works silently in the background to deliver a seamless customer experience while catching fraud attempts.

Device Fingerprinting

Device fingerprinting tracks unique device characteristics, such as the IP address, screen size, and browser type. By tracking device fingerprint data, banks can detect when an unfamiliar device is used or when a login attempt comes from a new location. Either scenario can indicate a fraudulent login attempt.

IP Geolocation

IP geolocation technology uses a device’s IP address to determine its geographic location. By monitoring IP addresses and their associated locations, banks can detect when a user logs in from a suspicious location, such as a different country or region.

Machine Learning

Machine learning is another powerful tool for detecting ATO fraud. By analyzing large quantities of data, algorithms can flag subtle changes and anomalies that might indicate fraudulent activity. Machine learning can also help identify new and emerging fraud trends, allowing banks to stay ahead of fraudsters.

Malware Detection

A strong malware detection solution can quickly identify suspicious programs or entities attempting to impersonate a legitimate account holder. The program can review malware against known threats and quickly analyze patterns for new or unfamiliar ones.

AI is central to advanced ATO fraud detection technology, and its role cannot be overstated. Advanced AI algorithms allow banks to analyze massive amounts of data in real time. They can detect suspicious behavior patterns and respond quickly to prevent fraud. AI also identifies new and emerging fraud trends, helping banks stay ahead of the curve and protect customers from harm.

Unleashing Machine Learning’s Power to Prevent Account Takeover Fraud

Machine learning is a game-changer in combating account takeover fraud. Let’s look at just some of the ways AI empowers fraud detection and prevention:

• Fraud Scoring: Machine learning algorithms can analyze large customer behavior datasets to identify patterns of fraudulent activity. By ranking each login or transaction by risk, they help fraud managers zero in on events that warrant further scrutiny.

• Anomaly Detection: Train machine learning algorithms to spot oddities in customer accounts. If a customer suddenly logs in using a new device or location, algorithms raise a red flag. This prompts fraud managers to investigate.

• Predictive Analytics: Harness machine learning to predict which customers are vulnerable to account takeover fraud. By analyzing customer behavior patterns, machine learning algorithms identify risky individuals and suggest proactive measures to prevent fraud.

• Continuous Learning: Machine learning algorithms evolve. This lets models adapt to new fraud patterns and improve their account takeover fraud detection and prevention over time. Constantly updating models with new data enables them to stay ahead of fraudsters’ tactics.

In a nutshell, machine learning improves banks’ ability to detect and prevent account takeover fraud quickly and accurately. By automating fraud detection and analysis, algorithms free fraud managers to focus on high-risk cases and proactive prevention.

“If left unchecked, ATO can take on a life of its own. Stopping this fraud in its tracks begins with decoding its DNA and building an understanding of a legitimate bank customer’s financial DNA.” — Dan Holmes, VP of Global Product Planning & Strategy, Feedzai

8 Must-Have Features for an Account Takeover Fraud Solution

Banks interested in a machine learning solution to detect and prevent ATO fraud should look for the following key features:

• Real-time monitoring. Choose an ATO solution that provides real-time transaction monitoring of all customer transactions and account activity. Real-time monitoring helps banks detect suspicious behavior quickly.
• Multi-factor authentication. Look for strong authentication measures, such as multi-factor authentication, to secure account access for authorized users only.
• Behavioral analytics. The account takeover fraud solution should use behavioral analytics to track user behavior patterns. Monitoring user behavior patterns allows banks to learn their users’ genuine habits and detect anomalies that could indicate fraud.
• Machine learning and AI. Choose a solution built on machine learning, AI, and federated learning. Machine learning constantly adapts to the fraud landscape and improves fraud detection, while federated learning lets institutions share threat intelligence collaboratively without exposing sensitive customer data.
• Device recognition. Identify whether an active session originates from a customer’s recognized, regular device to detect unknown access attempts quickly.
• Behavioral biometrics. Analyze subtle physical interactions such as mouse movements, keystrokes, and touch dynamics to confirm they align with the user’s normal habits.
• Agility. Choose an agile solution that lets your team respond swiftly and adapt defenses in real time during an active fraud attack.
• Location analysis. Detect when logins occur from unexpected geographic regions or flag physically impossible travel events within short timeframes.

In a nutshell, machine learning improves banks’ ability to detect and prevent account takeover fraud quickly and accurately. By automating fraud detection and analysis, algorithms free fraud managers to focus on high-risk cases and proactive prevention.

What to Do When an Account Takeover Occurs

If an account takeover bypasses your organization’s defenses, pivot the customer experience instantly from detection to containment. Here is a step-by-step roadmap for supporting impacted customers:

1. Stop Active Sessions. Freeze sensitive outbound movement (e.g., fast payments, wire transfers, card transactions), and force a credential reset across all digital banking channels. This stops further financial losses that require real-time automated intervention.
2. Communicate with Customers. Reach out to affected customers immediately through verified channels (e.g., SMS alert, push notification, or secure direct call) to inform them of the protective lockdown.
3. Link Analysis. Identify connected fraudulent activity across accounts, such as payments directed to the same beneficiary or multiple suspicious sessions initiated from the same device.
4. Remediate Financial Losses to Restore Customer Trust. Success in fighting fraud isn’t measured only by preventing monetary losses; it’s measured by customer retention. Promptly execute full account remediation, including guiding customers to reset user IDs and passwords to re-secure digital identity, while issuing temporary credits or filing claims for unauthorized transactions to protect long-term institutional loyalty.
5. Feed Data Back into Your Adaptive Defense Engine. Fraudsters are continuously refining their tactics. Make sure the intelligence gathered from one incident is instantly relayed to your machine learning models to harden defenses across your institution’s entire network.

How Feedzai Prevents Account Takeover Fraud

Protecting customers from ATO attacks requires moving beyond static, point-in-time identity checks. Relying on a correct password or single MFA code only proves someone typed the right characters. It does not prove who typed them or their true intent.

Feedzai closes this fundamental defense gap with our Digital Trust solution, which protects financial institutions by operationalizing continuous adaptive trust. Continuous adaptive trust treats identity as an evolving concept. We calculate confidence from the start of a digital session, re-evaluate it continuously, and weigh it against what the customer is actually trying to execute. Friction is introduced only when risk outruns confidence.

Key Components of Feedzai’s Digital Trust

Digital Trust brings together several core capabilities to safeguard customer accounts without adding friction. Here is how its key components work together to ensure comprehensive protection:

Behavioral Biometrics and Intent Analysis 

Feedzai silently analyzes thousands of subtle physical and behavioral signals in the background, including typing cadence, mouse dynamics, screen-swiping patterns, and navigation rhythms. By comparing real-time interactions against a customer’s unique profile,

Digital Trust instantly detects subtle anomalies. If a session exhibits uncharacteristic, robotic, or coercive behavior indicative of a bot, social engineering, or an unauthorized third party, risk scores elevate automatically.

Advanced Device Fingerprinting & Geolocation

Digital Trust captures deep situational signals, including device characteristics, IP geolocation, browser configurations, and network telemetry. It rapidly identifies unrecognized devices, proxy connections, or geographically impossible travel events, blocking access before bad actors can make non-monetary profile updates or drain funds.

Real-Time Continuous Anomaly Detection

Instead of evaluating activity in isolated silos, Feedzai aggregates behavioral, situational, and credential signals across the entire customer journey. By continuously monitoring session health from pre-login to post-login transactions, Digital Trust blocks ATO events in real time before funds leave the account.

By uniting these multi-layered signals into a single, cohesive view, Feedzai empowers banks to shift from reactive defenses to proactive, real-time protection, stopping account takeover in its tracks while preserving seamless digital experiences for legitimate users.

Additional Resources

• Webinar: How to Stop Evolving Account Takeover Threats in North America
• Report: Feedzai Scores High in Behavioral Biometrics
• Solution Sheet: Digital Trust for Account Monitoring
• Solution: Prevent Account Takeover with Behavioral Biometrics