AI fraud detection: 6 strategies to stop attacks in real time

According to the FBI’s 2025 Internet Crime Report, cyber scams cost Americans over $21 billion in 2025, an increase of 26% compared to 2024.

Fraudsters are continually improving their tactics by using AI to create more sophisticated scams at an ever-increasing scale. They're using AI to craft more convincing phishing emails, counterfeit websites, and deepfake videos.

It's not quite fighting fire with fire, but businesses can (and should) use the same AI technology to combat these advanced fraud tactics and financial crimes.

In this post, we'll explain and discuss six of the best AI fraud detection strategies.

Understanding AI fraud detection: What is it and how does it work?

AI fraud detection is the use of artificial intelligence technologies—including machine learning, natural language processing, and neural networks—to automatically identify and prevent fraudulent activities in real time. Unlike traditional methods like manual reviews or static rule-based systems (which rely heavily on predefined rules and human oversight), AI systems use ML models and other advanced technologies to learn, adapt, and improve over time.

The table below highlights the main differences between traditional and AI fraud detection.

Here's how AI works in fraud detection:

• Machine learning (ML): ML models are trained on historical transaction data to predict fraud. ML algorithms also analyze large datasets to find patterns and anomalies.
• Large language models (LLMs): Used for processing text data, such as emails or transaction descriptions, to identify suspicious language patterns.
• Natural language processing (NLP): Understands and interprets human language in communications to detect signs of fraud.
• Graph neural networks (GNNs): Map relationships between entities involved in transactions to spot unusual activities within networks.

AI fraud detection offers a modern approach to enhancing cybersecurity and preventing fraudulent transactions with increased accuracy and speed.

6 best AI fraud detection strategies

The pressure to detect and stop these attacks in real time has made AI-driven fraud detection a baseline expectation across industries. As we detail in Fingerprint's State of AI Fraud and Privacy Report, 41% of organizations are already facing AI-powered fraud attacks, with 99% reporting losses — averaging $414,000 per organization annually. 

Using AI in fraud detection doesn't rely on just one approach. The strategies we outline below focus on analyzing large datasets, recognizing patterns, verifying identities, and detecting anomalies for real-time fraud detection.

1. Data mining and analysis: AI algorithms sift through massive datasets to identify hidden fraud patterns faster than manual review. AI analyzes transaction histories, behavioral data, and other relevant information to predict and pinpoint fraudulent activities. Data mining software collects and processes this data so you have actionable insights as quickly as possible. Using AI algorithms for data mining and analysis helps detect unusual behaviors and can significantly reduce false positives and false negatives, saving time and resources.

2. Pattern recognition: AI learns typical user behavior patterns from historical data and flags deviations for review. For example, if a user who usually makes small purchases suddenly starts making large ones, the system would flag these transactions as possible fraud. Because pattern recognition algorithms are continuously updated with new data, these AI systems can easily adapt to new fraud tactics.

3. Identity verification: Advanced identity verification (IDV) processes help reduce payment fraud by ensuring the person making a transaction is who they say they are. AI tools and systems can assess the authenticity of user identities by cross-referencing multiple data points, such as verifying a user's information against their credit history, social media profiles, and other records. This strengthens authentication and account security measures for your business and builds trust for your users.

4. Biometric authentication: Biometric authentication uses unique biological traits like fingerprints, facial recognition, or voice patterns to verify identities. AI enhances biometric-check systems by making them more accurate and resistant to spoofing. For instance, facial recognition algorithms can detect subtle facial movements to ensure the presence of a real person, not just a photo or video. This reduces the chances of fraudsters bypassing security systems, making authentication processes more robust and reliable. However, it's important to note that fraudsters have recently begun using generative AI to try to bypass biometric authentication. They have used gen AI to identify deepfake voices and have also created moving images in attempts to spoof real humans and gain access to their accounts.

5. Machine learning: Machine learning involves training algorithms on vast amounts of data to recognize patterns and predict fraudulent activities. Supervised learning uses labeled datasets to teach algorithms the difference between legitimate and fraudulent transactions. Unsupervised learning, on the other hand, identifies unknown patterns without pre-labeled data. When fed high-quality data, machine learning algorithms improve over time and become more adept at spotting different types of fraud. This continuous learning process is crucial for maintaining an effective fraud detection system as fraudsters' techniques evolve.

6. Anomaly detection: Anomaly detection identifies irregularities that differ from normal consumer behavior in real time. For example, if a suddenly high volume of transactions originates from a single account or if there's an unexpected login from a different location, the system generates alerts. Anomaly detection has a lot in common with pattern recognition but specifically identifies irregular activity to raise the alarm. Anomaly detection uses both supervised and unsupervised learning to improve its accuracy, and giving the algorithm access to both these types of datasets makes it a more powerful tool for catching financial fraud as early as possible.

Challenges of using AI for fraud detection

While AI-powered fraud detection systems come with a lot of advantages, there are a few challenges to be aware of as you implement these tools. We'll outline some of them here.

Fraud detection is only as good as the AI's data

The effectiveness of AI systems in fraud detection heavily depends on data quality. Machine learning algorithms need high-quality historical data to learn patterns of fraudulent activity. If this data is biased or incomplete, the resulting predictive outputs are more likely to make inaccurate predictions.

Specifically, small, unclean datasets can lead to a high number of false positives. This negatively impacts both the customer experience and the overall fraud management system.

AI makes mistakes that aren't always obvious

AI algorithms aren't perfect. Even if you train the algorithms with high-quality data, AI systems can still make errors. False positives can lead to legitimate transactions being flagged as suspicious, causing unneeded frustrations for you and your customers.

And these mistakes are not always easy to catch. Recognizing inaccuracies requires regular monitoring and fine-tuning of the machine learning models powering the AI algorithms. This can be resource-intensive, both in terms of time and cost.

Fraudsters evolve their tactics faster than AI can keep up

Fraudsters are continually evolving their tactics to bypass security measures. Although AI systems are capable of improving with new training data, new scams require quick adaptations throughout the fraud prevention stack. This "cold war" between fraud detection solution providers and fraudsters makes it challenging to ensure consistent protection without consistent updates.

These challenges underscore the importance of ongoing vigilance and improvements in AI model and data management. High-quality data, diligent monitoring, and ability to adapt quickly to changes in the fraud landscape are key to effective AI fraud detection.

Device intelligence: Another way to detect and combat fraud

As we mentioned, as fraud detection becomes more advanced, so do the methods used by fraudsters. That's why businesses who want to stay ahead of the curve should take an innovative approach that incorporates AI with device intelligence and browser fingerprinting.

Device intelligence uses  an array of unique attributes, such as the operating system, installed languages, and keyboard layout, to identify a user's browser and device. This technique can recognize patterns and identify unusual activities. When combined with AI, browser fingerprinting can be a powerful tool in any fraud detection stack.

Browser fingerprinting offers businesses the ability to:

• Enhance online fraud detection. By capturing unique information from a device, browser fingerprinting can easily spot suspicious user behavior. This helps e-commerce sites, fintechs, and other financial institutions combat identity theft and fraud.
• Improve cybersecurity. Browser fingerprinting helps verify login attempts as legitimate, reducing the risk of unauthorized access.
• Reduce identity theft. By accurately identifying devices, it becomes harder for fraudsters to create multiple fake identities. (Check out our article on synthetic identity fraud and how to prevent it for more details.)

Using device intelligence can significantly strengthen account security, as well as your cybersecurity measures at the login point. This is essential as fraudsters get more sophisticated, especially with the use of AI in their scams.

AI vs. device intelligence: When to use each approach

While AI fraud detection and device intelligence both combat fraud, they work best in different scenarios and are most effective when combined:

AI fraud detection

• Best for: Analyzing transaction patterns, predicting emerging fraud trends, processing large datasets
• Strengths: Adapts to new fraud patterns over time, identifies complex behavioral anomalies
• Limitations: Requires quality training data, can produce false positives, slower to adapt to brand-new attack vectors

Device intelligence

• Best for: Real-time bot detection, identifying returning visitors to your site across sessions, detecting device spoofing
• Strengths: Immediate, deterministic signals; works without historical data; identifies devices even when cookies are cleared
• Limitations: Scoped to device and browser signals rather than transaction-level analysis

Combining both approaches creates a layered defense: device intelligence provides immediate, deterministic signals about the device and browser, while AI analyzes behavioral patterns and transaction data over time. For example, device intelligence can instantly flag a known bad bot, while AI can identify subtle patterns suggesting account takeover attempts across multiple sessions.

How Fingerprint enhances AI fraud detection

Fingerprint's device intelligence capabilities complement AI fraud detection by providing real-time, deterministic signals that AI systems can incorporate into their decision-making. Two key features are particularly valuable:

• Bot Detection: Returns good, bad, or notDetected to immediately flag automated activity. The bot detection signal identifies headless browsers, automation tools like Selenium or Puppeteer, and other bot traffic—allowing you to block automated fraud attempts before they reach your AI models.
• Suspect Score: A dynamic, weighted risk assessment that combines multiple Smart Signals into a single integer value. The more suspicious signals triggered (such as browser tampering or location spoofing), the higher the score. You can use Suspect Score to add friction for suspicious visitors or flag them for additional review.

These signals can be fed directly into your AI fraud models as additional features, improving their accuracy by providing device-level context that behavioral analysis alone might miss.