According to the FBI Internet Crime Complaint Center, phone and cyber scams cost Americans over $12.5 billion in 2023, an increase of 22% compared to 2022. 

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 even deep fake 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 uses artificial intelligence to spot fraudulent activities quickly and accurately. Unlike traditional methods like manual reviews or rule-based systems (which rely heavily on predefined rules and human oversight), AI systems use machine learning and other technologies to adapt and improve over time.

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

Traditional	AI
Sets fixed rules to flag suspicious activities	Learns from new data to identify patterns
Experts can customize it, but often miss complex schemes	More flexible and accurate for evolving fraud tactics

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. Generative AI platforms like ChatGPT fall into this category. 
• 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

AI fraud detection is becoming more commonplace, with an increasing number of businesses adopting the technology. In fact, according to Juniper Research, AI-enabled financial fraud detection attracted a global spend of $6.5 billion in 2022. 

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

Data mining and analysis involve sifting through massive datasets to extract useful information. AI algorithms can identify hidden patterns and correlations that humans might miss, and they can identify these patterns much faster than humans can.

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 can learn typical user behavior patterns from historical data. When transactions deviate from these patterns, the system flags them for further 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 processes can help reduce instances of payment fraud by ensuring the person making a transaction is who they say they are. This strengthens security measures for your business and builds trust for your users.

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.

4. Biometric authentication

Biometric authentication uses unique biological traits like fingerprints, facial recognition, or voice patterns to verify identities. AI enhances biometric 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 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. AI can monitor transactions in real time and flag any suspicious activities. Anomaly detection has a lot in common with pattern recognition but specifically identifies irregular activity to raise the alarm.

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 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 constant 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 uses AI and includes device intelligence like browser fingerprinting.

Browser fingerprinting helps identify unique characteristics of a user's browser and device, such as the operating system, installed languages, keyboard layout, etc. 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 ensures login attempts are legitimate, reducing the risk of unauthorized access.
• Reduce identity theft. By accurately identifying and tracking devices, it becomes harder for fraudsters to create fake identities. (Check out our article on synthetic identity fraud and how to prevent it for more details.) 

Using device intelligence can significantly strengthen risk management, 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.