Fraud Detection Systems for Identity Verification: How Do They Work?

Identity fraud is accelerating: According to our 2026 Identity Fraud Report, deepfakes are linked to 1 in every 5 biometric fraud attempt, while injection attacks have increased 40% year-over-year. Fraud-as-a-Service, where cybercriminals use the SaaS business model to provide tools, services, and support for a fee, is also on the rise, expanding the scale of these types of activities.

Fraud detection systems have become critical to protect entities from criminal access or theft of data and resources. Today’s biggest threats go beyond stealing data, to impersonating people. That’s why the strongest defense starts with identity.

Fraud detection software can monitor systems and detect signs of potential fraud, such as unusual user behavior, anomalous network activity, and IP address irregularities, then alert teams to potential issues and take proactive steps to stop them. These fraud detection solutions are especially essential in sectors characterized by frequent transactions and classified information, such as healthcare, finance, the government, as well as for large enterprises with complicated technical infrastructures.

• Fraud detection systems are critical as threats become more common and increasingly sophisticated, especially for organizations in highly regulated industries.
• Modern fraud detection software combines rules-based logic, behavioral analysis, and machine learning for a multilayered approach that provides protection for existing and emerging threats.
• AI-powered fraud is increasing to an unprecedented scale, targeting user identities rather than just transactions.
• Effective fraud monitoring in the current environment requires real-time detection and risk scoring, integration with identity and access systems, support for Zero Trust architecture, and alignment with compliance requirements.
• Solutions must also balance security with user experience to avoid excessive requirements and blocking errors.
• Identity-first fraud detection links every signal to a verified person, reducing false positives and missed threats.

In simple terms, fraud detection systems monitor transactions, behaviors, and credentials to identify potentially irregular and illegal activity in real time. These go beyond financial fraud and criminal transactions to include instances of identity misuse and stolen credentials. Over 40% of document fraud involves national ID cards, which can be used to create a fake identity or steal someone else’s.

The costs are high: According to one report, fraud cost U.S. businesses $114 billion in 2024 among the leaders surveyed, the equivalent of 9.8% of revenue.

Identity-focused fraud prevention in the form of fraud detection software can reduce losses, preserve system integrity, and enhance user trust. These solutions use methods like behavior analysis, identity signals, and risk assessment to constantly calculate the likelihood of fraud and analyze when to take action.

Fraud monitoring and detection systems are frequently deployed for operations where the importance of identity security is especially high. These include financial transactions, access to private data and systems, and sensitive or high-stakes approvals, such as for benefits disbursements or access to private data.

Fraud detection systems layer various approaches to build a comprehensive risk profile across a network.

Rules-based logic forms the first line of defense, flagging obvious anomalies like impossible travel (such as multiple log-in attempts from different countries within minutes) or large transactions outside of normal business hours. These actions may indicate that a user’s identity has been hacked or stolen, or that someone is employing a synthetic identity. This approach can also incorporate information about compromised password databases to flag potentially stolen credentials. However, it can only identify known patterns.

Behavioral analysis is another layer. It looks at how users interact with systems and notes any deviations in habits, such as changes in device characteristics, login location or time, navigation patterns, or session duration. Because behavioral biometrics create unique user signatures, they are difficult to replicate, even with stolen credentials.

Finally, machine learning analyzes large datasets to detect pattern deviations, enabling it to identify subtle changes that the other layers can miss. This method is especially essential for its ability to learn and adapt, especially as criminals increasingly turn to AI to hone tactics.

• Machine learning may be supervised, meaning it uses labeled data (past examples of fraudulent and legitimate activity) to train models that predict the likelihood of fraud in new transactions. It’s typically used when there’s sufficient historical data for models to learn from.
• Unsupervised machine learning detects potential fraud by finding unusual patterns or anomalies in data without needing labeled examples. It can be used when there isn’t enough historical data or to identify new and emerging fraud tactics, such as synthetic identities and deepfakes.

Hybrid fraud detection combines machine learning models with rules-based business logic for the most accurate, adaptable, and responsive approach.

Using these tactics, the fraud detection system decides when a risk merits intervention. When thresholds are exceeded, they trigger alerts or step-up authentication challenges, balancing security with a smooth user experience for lower-risk users and scenarios.

Fraud Detection vs. Fraud Monitoring: What’s the Difference?

Fraud monitoring refers to the passive observation of online activities, such as transactions or user logins. When used alone, this requires teams to analyze the activities and manually flag potential issues.

The size and scale of many large businesses and organizations, such as banking networks and government agencies, make monitoring alone insufficient. It’s all too easy for suspicious behavior and anomalies to fall through the cracks. And if credential theft or misuse is involved, it may slip by altogether and escalate in seconds.

Fraud detection, on the other hand, takes a proactive approach by sounding the alarm when risks meet certain levels. At that point, the system triggers an automated alert or response.

Many legacy systems in place monitor without the ability to intervene in real-time. Today’s highly sophisticated threats and sprawling enterprise infrastructures require systems that can identify issues as they arise and take action immediately. By focusing on verifying user identities, modern systems provide faster, more accurate, and efficient detection of potential fraud.

Various high-security sectors use fraud detection systems to flag identity-driven attempts at infiltrating their systems.

Financial institutions use adaptive authentication to verify customer identities before enabling account access or approving large transactions. Methods include device fingerprinting and behavioral analytics to flag unusual login patterns or transactions. This helps prevent account takeover attempts, payment fraud, synthetic ID use, and the use of compromised credentials.

Fraud detection software helps government agencies to protect digital portals to services, such as Social Security and Medicare systems, by combining multiple layers of identity intelligence. It can analyze login behavior and device fingerprints to detect anomalies that suggest identity theft or account takeover attempts. This ensures that only legitimate users can access sensitive benefits information and personal data or make changes to their accounts.

Healthcare organizations rely on fraud detection software to identify unauthorized access to EHR systems or patient portals. For example, fraud detection systems can flag anomalies in insurance claims, demographics, or medical history that suggest someone is using a stolen identity to get care or prescriptions.

Large enterprises with complex networks and dispersed or hybrid workforces depend on fraud detection software to help them stay on top of potential fraud attempts. For businesses that sell goods or services online, these tools monitor customer payment activity to identify potentially stolen cards, unusual purchase patterns, or synthetic identities.

Fraud detection monitoring systems in these sectors not only incorporate strong identity verification but also take industry-specific nuances and factors into account. Organizations can adapt fraud detection workflows according to their specific policies, triggering adaptive authentication requirements in response to different events or series of events.

The latest generation of fraud tactics is costing organizations more than ever as new tactics target user identities, not just the system. Nearly 60% of businesses in the U.S. reported greater losses due to fraud in 2025, while 72% expect AI-generated fraud to be a major challenge.

Highly sophisticated phishing attacks are often conducted at scale with AI, leading to greater success. “We are seeing the use of generative AI to create fake identities, whether it's generating faces, or whether it's using existing people's faces and using them as part of deep fakes, or whether it's phishing attempts, which could involve AI generated voices,” says Simon Horswell, Fraud Specialist Senior Manager at Entrust. Criminals often push multiple MFA requests, causing user fatigue and frustration that can lead to errors. Stolen user data is used to create deepfake IDs, which are getting harder and harder to tell from authentic versions.

Unfortunately, traditional fraud monitoring tools and legacy systems often fail to catch these attempts, which can cause a lot of damage fast. Modern fraud detection software provides the continuous, context-aware monitoring that incorporates identity authentication as a core layer of defense, linking every access attempt, or transaction to a verified user identity and assessing its risk in real time.

However, teams must look out for potential pitfalls in solutions, such as:

• False positives that flag legitimate users as suspicious
• False negatives that fail to identify fraud due to insufficient identity verification
• Latent detection that can’t keep up with fast-moving and automated fraud attempts
• Siloed data and incompatible tools that make integration with existing systems complex and unreliable
• Over-aggressive controls that cause user friction and a negative experience 

Effective fraud detection monitoring systems combine a layered approach to identity verification, such as combining device fingerprint with behavioral analytics; real-time risk scoring; and workflows that adapt to contextual clues. “We've got risks that are starting to emerge at every single stage in the financial transaction,” Horswell says. “You should be weaving AI into every single one of these layers to augment and reinforce the security that you've already got in place.”

Entrust’s approach to fraud detection is built on the principle that if you can’t verify the person, you can’t verify the interaction. That’s why our comprehensive identity verification solutions combine features like identity proofing, digital credentialing, and biometric authentication to help organizations reduce opportunities for fraud—without creating friction for authentic users.

Our fraud detection solution is powered by our award-winning AI and trained on both genuine and fraudulent documents. This approach reduces missed fraud cases by 54% compared to other machine learning approaches trained only to spot cases of fraud they’ve already seen.

Learn more about how Entrust’s identity-centric security solutions can help your organization fight fraud and cyberthreats at every stage of the identity lifecycle.