Machine Learning for Fraud Detection and Prevention

Fraud detection is rapidly evolving with machine learning (ML) leading the way. Industries like finance and e-commerce are using ML to fight fraud more effectively. For example, ML has greatly reduced credit card fraud, cut Citibank’s phishing attacks by 70%, and lowered Walmart’s shoplifting by 25% through real-time video analysis.

In this article, we’ll dive into the benefits and challenges of using machine learning for fraud detection, explain how the technology works, and provide you with essential insights to help protect your business.

The Difference Between Machine Learning and Artificial Intelligence

The terms AI (Artificial Intelligence) and machine learning are frequently used as if they mean the same thing, but they represent distinct concepts. While all forms of machine learning are part of AI, not all AI systems rely on machine learning.

AI is a broad concept focused on creating machines that mimic human thinking, like reasoning and problem-solving. It includes techniques that don’t always involve learning from data. On the other hand, machine learning, a part of AI, allows machines to improve by analyzing data and making decisions without needing to be specifically programmed for each task.

Furthermore, deep learning is a more advanced form of machine learning. It uses neural networks, inspired by the human brain, to handle large amounts of data and make complex decisions. Deep learning is especially effective in tasks like image recognition, language processing, and areas where simpler algorithms have difficulty.

How Does Fraud Detection with Machine Learning Work?

In online fraud detection, machine learning uses AI algorithms trained on your historical data to detect suspicious activity. These algorithms analyze past instances of fraud and legitimate transactions to create risk rules that can block or allow actions like logins, identity verification, or purchases. The more data the system processes, the more accurate it becomes in identifying fraud.

To train machine learning models effectively, it’s crucial to flag both fraudulent and non-fraudulent cases. This helps reduce false positives and ensures the risk rules are more precise over time. As the system learns, it adapts to new fraud tactics and becomes better at catching fraud before it impacts your business.

By automating fraud detection, machine learning helps businesses act quickly, reducing fraud while ensuring a smooth experience for legitimate users.

Examples of Machine Learning for Fraud Detection

AI-driven fraud prevention is industry-agnostic. It only needs data to work, which is why you’ll find it has been deployed in a variety of verticals such as:

• Retail stores: Analyzing thousands of transactions can be challenging, prompting eCommerce sites to use machine learning to identify unflagged fraudulent transactions. Juniper Research predicts a $50.5B fraud loss for online retailers by 2024. ML systems help identify targeted items, risky shipping information, and questionable card payments to reduce chargebacks.
• Financial institutions: Fintech companies and insurers must meet compliance requirements to avoid fines while processing quickly to stay competitive. Machine learning helps distinguish legitimate users from fraudsters, preventing fraudulent profiles from slipping through.
• iGaming companies: Online gaming platforms must ensure player authenticity and manage high-value rewards. In 2021, online gambling identity fraud increased by 43%. Machine learning detects suspicious behavior, identifying poker bots, cheating players, and low-quality affiliates.
• BNPL: Buy Now Pay Later accounts function like digital wallets, vulnerable to account takeover attacks. Machine learning analyzes login data to enhance user authentication, preventing unauthorized purchases.
• Payment gateways: Payment gateways must quickly process transactions, making manual reviews impractical. Machine learning detects fraudulent transactions, reducing chargeback costs.

Benefits of Machine Learning for Fraud Detection

Because machines have a much easier job processing a large dataset than humans, what you get is the ability to slice and dice huge amounts of information. That means:

• Faster and more efficient detection: The system gets to quickly identify suspicious patterns and behaviors that might have taken human agents months to establish.
  
• Reduced manual review time: Similarly, the amount of time spent on manually reviewing information can be drastically reduced when you let machines analyze all the data points for you.
  
• Better predictions with large datasets: The more data you feed a machine learning engine, the more trained it becomes. That is to say, while large datasets can sometimes make it challenging for humans to find patterns, it’s actually the opposite with an AI-driven system.
  
• Cost-effective solution: Unlike hiring more RiskOps agents, you only need one machine-learning system to go through all the data you throw at it, regardless of the volume. This is ideal for businesses with seasonal ebbs and flows in traffic, checkouts, or signups. A machine learning system is a great ally to scale up your company without increasing risk management costs drastically at the same time.

Algorithms work 24/7 without breaks, unlike human fraud managers who may face a backlog of manual reviews. Machines can streamline the process by quickly sorting obvious fraudulent or legitimate cases.

Differences Between Blackbox and Whitebox Machine Learning

While machine learning tends to be a selling point for most fraud prevention vendors, not all solutions are created equal. Notably, there is a key difference between whitebox and blackbox machine learning:

• Blackbox machine learning: The system is designed to work in a “set and forget” mode, where the decisions are opaque and automated. It can be great for small businesses that do not need to dive into the nitty-gritty of tweaking their risk rules.
  
• Whitebox machine learning: The system will give you clear explanations as to why a risk rule was suggested. This makes it easier to understand where the risk is and gives fraud managers more flexibility to improve their fraud prevention strategy. 

How to Use Machine Learning for Fraud Prevention

The term machine learning may seem intimidating, but getting started with an algorithmic system is actually straightforward. 
In this example, we’ll be looking at reducing transaction fraud (and therefore chargeback costs).

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Testing rules on historical data

Good fraud prevention software should allow you to revisit past cases to see if the rules would have helped. This is done in a sandbox environment, where you can switch the rules on and off and witness their accuracy in person. 

Running a test will create a confusion matrix based on previous transactions over the selected time frame, which highlights the rule’s estimated accuracy rate.

In machine learning, a confusion matrix, or error matrix, is a table layout that visualizes an algorithm’s performance. It allows you to calculate accuracy over a specific date range—selectable from the last hour through to the last year.

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Outsourced vs On-Site Machine Learning Fraud Detection

While building machine learning models in-house with a skilled team is possible, it’s important to consider the significant time, effort, and costs involved:

• Talent acquisition costs: Hiring data scientists, engineers, and machine learning specialists is necessary to develop the models.
  
• Data preparation time: Cleaning and preparing raw data is a time-consuming process, taking up 60 – 80% of the entire timeline from receiving input to suggesting risk rules.
  
• Benefit of shared data: Outsourced machine learning engines can leverage shared data from multiple clients. While rules aren’t universally applied, vendors can use industry knowledge to develop highly accurate rules that benefit all parties.
  
• Integration challenges: Integrating machine learning into a risk management strategy can require substantial time and investment.

On the other hand, outsourced machine learning fraud detection leverages shared industry data for better accuracy and offers easier integration.

How SEON’s Machine Learning Is Used Against Fraud and Money Laundering

Embracing a machine learning system for fraud and money laundering prevention offers numerous advantages. However, the goal isn’t always to simply approve or block actions but to equip risk analysts with timely and comprehensive information. There are situations that fall into grey areas where algorithms alone may struggle.

SEON’s whitebox machine learning system excels by using historical data to suggest new rules, offering full transparency and human oversight. This approach allows analysts to refine and test rules in a controlled environment, combining AI’s processing power with human decision-making. SEON’s balanced strategy merges machine learning with human insight, making it a powerful tool in combating online fraud and malicious activities.

Frequently Asked Questions

You might also be interested in reading about:

• SEON: The 6 Best Transaction Monitoring Software & Tools
• SEON: What Is Whitebox Machine Learning?
• SEON: Guide to Fraud Scoring: What Is It and How Does It Work?

Related Source for this article

• Data Preprocessing vs Data Wrangling in Machine Learning Projects
• Juniper Research forecasts a $50.5B loss to fraud for online retailers by 2024
• TransUnion gaming report: 2021 saw a 43% increase in online gambling identity fraud
• GetOnData: The Future Of Security: Impact Of Machine Learning In Fraud Detection