AI for Fraud Detection: How Machine Learning Prevents Payment Fraud in Real Time

With machine learning, it’s possible to analyze vast stores of data in minutes. While fraudsters are using artificial intelligence (AI) for criminal purposes, financial institutions can stay one step ahead with AI for fraud detection.

As a fraud detection tool, AI is a necessity in today’s environment. Criminals don’t wait to probe your company’s defenses or to test stolen credit cards. Monitoring and catching each incident would be impossible without the latest tools and technology. Machine learning and AI can analyze vast stores of information in a heartbeat, allowing you to quickly detect red flags. 

TL;DR

• Machine learning tools can process huge amounts of data at a speed that would be impossible for humans.
• Today’s criminals use AI to stage clever, large-scale attacks. Financial institutions need powerful AI defenses to counteract these attacks. 
• AI models can detect synthetic identity clusters, such as multiple cards sharing the same information.
• Odd timing, sudden spikes in spending, and using a VPN are typically considered red flags by AI.
• Behavioral biometrics, like touchscreen pressure and typing speed, can help with detecting bots and account takeovers. 
• AI uses supervised and unsupervised learning to train on datasets.

Humans have a role in the future of AI. They must create datasets, shape decision-making, and conduct AI model reviews.

How Are AI and Machine Learning Used for Fraud Detection in Payments? 

In the finance industry, AI fraud detection is the term for using machine learning technology for detecting fraud. Large datasets are given to AI models. By scanning through the dataset of real and fraudulent transactions, the model is able to learn the red flags that indicate potential fraud.

For example, here are a few common red flags AI might pick up on.

• Suspicious Timing: The user never shops at night, but the transaction happened at 2:00 AM. 
• Synthetic Identity Clusters: Cards that appear to be unrelated share the same phone number or contact information.
• Behavioral Biometrics: Typing speed, mouse movements, and touchscreen pressure can indicate if the account has been taken over by a bot or cybercriminal.
• Sudden Splurges: When a spending spike occurs with a user who normally makes small purchases, it is a major red flag.
• Device Fingerprinting Concerns: AI may become suspicious if there are device fingerprinting issues, such as a device that uses a VPN.

Today, many financial institutions are implementing AI and machine learning into their processes. These tools help them make better decisions, manage risk, and prevent fraud. Plus, AI can use predictive analytics to forecast how each user will likely make purchases in the future, making it easier to spot unusual transactions.

How Does AI Work in Fraud Detection? 

To protect financial accounts from fraud, AI has multiple tools available. These approaches can be divided into supervised and unsupervised learning methods.

Supervised Learning

This kind of fraud detection and AI involves training a model on labeled data. It can detect patterns in the data in order to catch different types of fraud. 

For example, logistic regressions and decision trees are more traditional forms of machine learning. They are designed to classify and evaluate variables. While logistic regressions are a probability-based approach, decision trees evaluate different variables at the same time to create non-linear relationships between them.

In comparison, neural networks use deep learning models to analyze data. These tools are so complex that they can learn from historical data, conduct image recognition, and catch fraud in real time.

Unsupervised Learning

 Unsupervised learning is when AI is given unlabeled data to learn from. In finance, this style is especially important because it means AI’s fraud prevention algorithms can spot zero-day attacks that haven’t been seen or labeled before. Anomaly detection, autoencoders, and clustering are all different types of unsupervised learning that can detect fraudulent transactions.

How Does Machine Learning Work in Fraud Detection?

Machine learning is really a subset of AI. It powers most fraud detection because of its ability to spot patterns and detect outliers. Once patterns are fed into the machine learning system, it can turn the raw inputs into features, which can be processed and understood. For example, an invoice can be converted into the dollar amount, time of day, and customer number.

 When a payment transaction arrives at your company, the machine learning tool will analyze it to see if it fits common risk patterns. Then, the transaction can be approved, rejected, or sent for an extra verification step. This ensures low friction for genuine transactions and an extra step for high-risk transactions.

Rules-Based vs. AI Fraud Detection

To get a better understanding of how fraud detection and AI are different from traditional rules-based fraud detection, let’s look at a comparison of the two styles.

Examples of AI in Action

There are many different fraud patterns and attack types that AI can spot. From verification chatbots to crypto tracing, AI can prevent fraudulent transactions and detect real-time cases of fraud.

• Crypto Tracing: Because cryptocurrency is decentralized, it can be more prone to cyberattacks. With AI fraud prevention, these transactions can be monitored for suspicious activity, such as unusually fast transfers.
• Ecommerce Fraud: Banks can analyze purchase history, customer behavior, and other factors to spot fraudulent ecommerce purchases. They can also use AI tools to preemptively warn customers not to purchase from sites that exhibit red flags.
• Synthetic Identities: When fraudsters create new identities, AI can spot inconsistent email patterns for the region, reused device fingerprints on new accounts, and thin account histories.
• Verification Chatbots: These chatbots can analyze language patterns and behavior patterns in chats to spot scam artists.
• Account Takeover: AI can spot new credentials, shipping address changes, password resets before large purchases, and other behaviors to figure out when an account takeover has occurred.
• Card Testing Attacks: Fraudsters may run many small-dollar transactions to see if a bunch of stolen cards are valid. AI looks for multiple cards from a single device, unusually fast checkout completions, and similar factors to discover card testing attacks.

Is There a Role for Humans in AI Fraud Detection?

As technology evolves, there will continue to be a role for humans in fraud investigations. AI isn’t just a magic black box. It is a tool that has to be pointed in the direction it needs to go.

Even the best fraud detection AI can only be as good as the data you give it. If your fraud labels are inaccurate or you use poor synthetic data, it will affect the AI’s ability to do its job. Instead of spotting accurate patterns, it will find incorrect patterns and let real fraud slip through the cracks.

The humans involved are responsible for creating and feeding AI the best data possible. As the AI continues to learn, you’ll need to define its risk strategy. Specialists are also in charge of conducting manual reviews and monitoring the performance of the system.

Future Developments in Fraud Detection: Going Beyond AI

Today, there are still many challenges faced by AI. Thanks to banking’s data security rules, AI and its data-based training sets must be used in a way that maintains data privacy. Hallucinations and bias are also ongoing issues.

While there are challenges that need to be overcome, exciting new developments are already underway.

• Better Fraud Detection: As time goes on, better device fingerprinting and identity verification will allow AI to detect fraudulent transactions earlier in the process.
• Adaptive Verification and Authentication: Another key change will be in how AI requests additional verification. Over time, it will become better at determining when an account is high risk and requires additional verification so that low-risk accounts can enjoy frictionless transactions.
• Explainable AI: AI is already creating outputs that are challenging for computer scientists to explain the reasoning behind them. Eventually, there will be new tools that translate AI decisions into language that humans can readily understand.
• Enhanced Network-Level Intelligence: By sharing information within a network of merchants and payment processors, AI can identify fraudsters and criminal enterprises much faster.

How PayCompass Offers Advanced Fraud Detection Tools 

Whether you’re struggling with friendly fraud or want to reduce your false positive rate, PayCompass can help. We offer advanced fraud tools and risk controls with all of our payment solutions. During an initial evaluation, our team can help you determine which fraud prevention tools make sense for your needs.

We can help with real-time risk scoring, configuring rules, and monitoring fraud trends. With these tools, you can reduce the rate of false positives and ensure that fraudulent transactions are immediately stopped. From designing operational best practices to implementing AI-based fraud detection, our payment experts can help you take the next step in improving your company’s payment security.

Final Thoughts

A well-trained AI model is an excellent addition to your fraud prevention and mitigation program. However, it shouldn’t be the only one. To prevent fraud, it’s essential to use more than one fraud detection tool. Criminals are becoming increasingly sophisticated, so you can’t rely on one security measure and hope for the best.

Through AI for fraud detection, you can prevent chargebacks, fraud, and costly losses. By enhancing your AI fraud detection approach, you can score risk levels in real time, avoid false positives, and prevent financial losses. 

Learn more about the advantages of AI in fraud detection by reaching out to PayCompass today.