Fraud Detection Machine Learning

Fraud Detection Machine Learning: a “Smart” Move for Merchants

Electronic payments are your lifeline as an online merchant. But, while payments technology has made rapid growth possible in the eCommerce space, it has also opened the door to new threats.

We’re seeing a wave of new fraud attacks in the wake of the Covid-19 pandemic. In fact, consumers reported losing over $3.3 billion to fraud in 2020. That's a near-50% increase over the previous year.

Fraudsters are getting smarter. Fraud tactics are becoming more complex.

Advancements in technology have helped create this problem…but they may also provide the solution. As some merchants are starting to discover, fraud detection machine learning technology can help stop fraud before it happens in many cases.

In this post, we’ll discuss what this technology is and how it works. We’ll outline the benefits, and show you some areas where it may—or may not—help you stop criminals.

What Is Fraud Detection Machine Learning?

Fraud detection machine learning

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The machine learning model of fraud detection  is a technology-powered strategy that compares incoming information to historical data, prior to approving a transaction. The machine learning model uses sophisticated algorithms to analyze results, effectively “learning” from new input.

Machine learning (ML) is exactly what it sounds like: computers using experience and data to automatically improve their own capabilities.

The program tests incoming information to see if it either contradicts or reinforces an existing algorithm, then adjusts accordingly. The more data the machine receives, the more reliable its predictions will be.

How Does Machine Learning Detect Credit Card Fraud?

Machine learning is often described as "artificial intelligence." This is because it appears to give computers the ability to learn the same way that humans learn. In reality, instead of trying to mimic human thinking, machine learning simply compares facts and draws the most logical conclusions.

The computer is not literally “thinking” in the same way as a human. However, every new bit of information helps refine the algorithm. This makes the computer “smarter” in a real sense.

ML models are already known for their ability to help businesses breakdown data and create specific, measurable, attainable, realistic, and timely (or “SMART”) goals and action plans. For fraud detection, specifically, they can be trained using examples of good and bad transactions. The system gets better and better at identifying fraud activity in real time with more information.

How does a machine learning model for fraud detection work, though? As we’ll explore below, there are essentially two stages to the process: building the model, and implementing it.

Creating a Fraud Detection Machine Learning Model

You need as much transaction data as possible to start the process. This will establish a baseline for acceptable customer behavior. If your data set is too small for accurate learning, some providers will create “starter sets” of data from businesses similar to yours.

Next, the machine learning fraud detection system will pull specific data points from each transaction and add them to the model. This may include personal customer information, order and payment details, the location and network of the order, and so on. For a fraud detection model, all this information will need to be labeled as “good” or “bad.”

You now have the raw data to build your model. However, you still need to create an algorithm that helps the machine recognize the difference between “good” and “bad” transactions. Basically, you have to teach it the parameters for determining the legitimacy of a transaction.

Using a Fraud Detection Machine Learning Model

ML technology extracts key information from a transaction prior to accepting the order. The tool then tries to match the transaction against the data points used to create the model. This enables an apples-to-apples comparison between the new order and historical data.

The model calculates a score that reflects the transaction’s fraud risk based on everything it learns. This fraud score is a lot like a consumer credit score. It is compiled from multiple elements, with different factors weighted more heavily than others.

The final score is used to make a decision: accept the transaction, reject it, or flag it for manual review by a human.

The entire decisioning process typically takes less than a second. The customer is totally unaware that it even happened. The information extracted from the transaction is then fed back into the model, further refining the algorithm.

Advantages of Using Machine Learning to Detect Credit Card Fraud

Machine learning is only one part of a fraud detection system. It works extremely well in that regard, though.

As we established, machine learning fraud detection technology works by comparing new information against what it already knows. Humans go about the decision making process in the same way, but technology offers a few advantages:

Machine learning fraud detection offers advantages over traditional, rules-based fraud solutions as well.

First, rules have to be changed as fraud evolves. The system depends on absolute “yes/no” answers. So, you must constantly monitor, review, and update the technology manually. A fraud detection machine learning model, however, is designed to adapt to new information on its own.

There is also the issue of false positives. Because rules only allow “yes” or “no” decisions, it’s not uncommon for legitimate orders to get marked as fraud. This is a serious concern, as false declines cost merchants $443 billion every year. Ironically, that’s more than ten times greater than the actual cost of credit card fraud.

Fraud Detection Machine Learning Applications

It’s helpful to see how an ML fraud detection model can work on a practical level. So, let’s look at some examples of how it can be applied in some real-world scenarios.

Account Takeover

Email Phishing

Synthetic Fraud

Stolen Credit Cards

These are just a few examples. However, they help illustrate a wide range of advantages offered by machine learning fraud detection technology.

Disadvantages of Machine Learning Models

Done correctly, fraud detection machine learning is a highly effective way to identify and prevent fraud. That doesn’t mean it’s infallible, though.

Fraudsters work tirelessly to find new ways to subvert the system. For example, finding new ways to mimic typical customer behavior. This can make any fraud indicators much more subtle and harder to recognize.

There’s also the fact that machines are only as good as the input they have. Any bad data can impact the algorithm results. And since transaction info is fed back into the model, it can cause serious issues over time. For example, if your system misidentifies a friendly fraud incident as genuine criminal fraud, that skews the decisioning matrix. Inaccurate data leads to bad decision making, so the ML system will keep making the same mistake over and over.

The Proper Role of Machine Learning Technology

Machine learning has an important role to play in ensuring data integrity and identifying post-transaction threats like friendly fraud, return fraud, and cyber shoplifting. That role is very different from conventional fraud detection machine learning, though.

Using machine learning for more intelligent chargeback source detection lets you:

• Look beyond reason codes to find the true sources of chargebacks
• Be more proactive about future disputes
• Identify new revenue opportunities
• Reduce fees, overhead, and other costs
• Eliminate false positives and accept more transactions

An end-to-end solution powered by machine learning fraud detection technology is the only way to see true revenue recovery and sustainable growth. Learn more today