The Latest Trend in Fraud Fighting: AI for Fraud Detection

According to the FBI Internet Crime Complaint Center, telephone and cyber fraud will cost Americans more than $12.5 billion in 2023, a 22% increase from 2022.

Fraudsters are constantly improving their tactics by using AI to create increasingly sophisticated scams on an ever-increasing scale. They are using AI to create more convincing phishing emails, fake websites, and even deepfake videos.

It’s not entirely a case of fighting fire with fire, but companies can (and should) use the same AI technology to combat these advanced fraud tactics and financial crime.

In this post, we 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 detect fraudulent activity quickly and accurately. Unlike traditional methods such as manual review or rule-based systems (which rely heavily on pre-defined rules and human oversight), AI systems use machine learning and other technologies to adapt and improve over time.

The table below shows the key differences between traditional fraud detection and AI fraud detection.

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

This is 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 data sets to find patterns and anomalies.
• Large Language Models (LLMs): Used to process 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.
• Graphical Neural Networks (GNNs): Map relationships between entities involved in transactions to discover unusual activity within networks.

AI fraud detection provides a modern approach to improving cybersecurity and preventing fraudulent transactions more accurately and quickly.

6 Best AI Fraud Detection Strategies

AI fraud detection is becoming increasingly common, with more and more companies embracing the technology. According to Juniper Research, AI-enabled financial fraud detection is expected to attract $6.5 billion in revenue globally in 2022.

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

1. Data mining and analysis

Data mining and analytics involve sifting through huge data sets 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 locate fraudulent activity. Data mining software collects and processes this data so you have actionable insights as quickly as possible.

By using AI algorithms for data mining and analysis, you can detect unusual behavior and significantly reduce the number of false positives and false negatives, saving you 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 normally makes small purchases suddenly starts making large purchases, the system flags these transactions as possible fraud.

Because pattern recognition algorithms are constantly 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 that the person making a transaction is who they say they are. This strengthens your business’s security measures and builds trust with your users.

AI tools and systems can assess the authenticity of user identities by comparing multiple data points. For example, they can compare a user’s information with their credit history, social media profiles, and other data.

4. Biometric authentication

Biometric authentication uses unique biological characteristics such as fingerprints, facial recognition or voice patterns to verify identities. AI improves biometric systems by making them more accurate and resistant to spoofing.

For example, 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 chance of fraudsters bypassing security systems, making authentication processes more robust and reliable.

However, it is important to note that fraudsters have recently started using generative AI to bypass biometric authentication. They have used generative AI to deepfake voices and have also created moving images in attempts to spoof real people and gain access to their accounts.

5. Machine learning

Machine learning involves training algorithms on large amounts of data to recognize patterns and predict fraudulent activity.

Supervised learning uses labeled data sets 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 with high-quality data, machine learning algorithms improve over time and become better at spotting different types of fraud. This continuous learning process is crucial to maintaining an effective fraud detection system as fraudsters’ techniques evolve.

6. Anomaly detection

Anomaly detection identifies irregularities that deviate from normal consumer behavior. AI can monitor transactions in real time and flag suspicious activity. Anomaly detection has much in common with pattern recognition, but specifically identifies irregular activity to raise the alarm.

For example, if there are suddenly many transactions from one account, or if someone unexpectedly logs in from another location, the system will generate alerts.

Anomaly detection uses both supervised and unsupervised learning to improve accuracy. By giving the algorithm access to both types of data sets, it becomes a more powerful tool to detect financial fraud as early as possible.

Challenges in using AI for fraud detection

While AI-powered fraud detection systems offer many benefits, there are a few challenges you should be aware of when implementing these tools. We’ll outline a few of them here.

Fraud detection is only as good as the AI ​​data

The effectiveness of AI systems in fraud detection depends heavily on data quality. Machine learning algorithms require 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, biased data sets can lead to a high number of false positives, negatively impacting both the customer experience and the overall fraud management system.

AI makes mistakes that are not always obvious

AI algorithms are not perfect. Even if you train them with high-quality data, AI systems can still make mistakes. False positives can lead to legitimate transactions being flagged as suspicious, causing unnecessary frustration for you and your customers.

And these errors aren’t always easy to spot. Spotting inaccuracies requires constant monitoring and fine-tuning of the machine learning models that power the AI ​​algorithms. This can be resource-intensive, both in terms of time and cost.

Fraudsters are evolving their tactics faster than AI can keep up

Fraudsters are constantly evolving their tactics to circumvent security measures. While AI systems can be improved with new training data, new scams require rapid adjustments to the fraud prevention stack. This “cold war” between fraud detection solution providers and fraudsters makes it difficult to ensure consistent protection without consistent updates.

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

Device Intelligence: Another Way to Detect and Combat Fraud

As we’ve said, as fraud detection becomes more sophisticated, so do the methods fraudsters use. That’s why businesses that want to stay ahead of the curve need to take an innovative approach that leverages AI and incorporates device intelligence, such as browser fingerprinting.

Browser fingerprinting helps identify unique characteristics of a user’s browser and device, such as 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 allows companies to:

• Improve online fraud detection. By capturing unique information from a device, browser fingerprinting can easily identify suspicious user behavior, helping e-commerce sites, fintechs, and other financial institutions combat identity theft and fraud.
• Improve cybersecurity. Browser fingerprinting ensures that 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. (Read our article on synthetic identity fraud and how to prevent it for more information.)

Using device intelligence can significantly strengthen risk management as well as your cybersecurity measures when logging in. This is essential as fraudsters become increasingly sophisticated, especially with the use of AI in their scams.