One reason why fraud is so difficult to combat is that it’s always changing and evolving. Traditional rule-based systems for detecting fraud can’t keep up with new techniques and methods that fraudsters use to steal financial data. 

However, machine learning for fraud detection and prevention is proving to be an extremely effective solution. Companies that learned how to use machine learning for credit card fraud detection are able to mitigate the risk of fraud exponentially. 

For instance, PetSmart, a US-based specialty retailer, managed to save up to $12 million by using AI in fraud detection. PetSmart implemented an AI/Machine Learning technology to aggregate millions of transactions and compare each transaction against the aggregate data from all other transactions to determine its legitimacy.

A recent study by Capgemini found that nearly 70% of organizations believe that they will not be able to respond to cyberattacks without AI. Integrating AI—and more specifically machine learning—into fraud detection and monitoring can help businesses save resources, lower risk, and work more efficiently and accurately. Here’s how to use machine learning for fraud detection. 

Background: Machine learning vs AI

Machine learning and artificial intelligence are often mentioned in the same sentence—and even used interchangeably. Let’s start with a breakdown of machine learning and how it differs from AI in fraud prevention. 

Artificial intelligence (AI) is the science and approach to developing technology that mimics human intelligence. In practice, AI includes programming systems to acquire information, creating rules for using information, using the rules to reach conclusions, and evaluating outcomes to predict or compensate for errors. 

There are many ways of integrating AI in fraud prevention, such as:

  • Fraud scoring models that use artificial intelligence to analyze a variety of data points, such as customer behavior, transaction history, and device information, to assign a risk score to each transaction. 
  • Fraud detection chatbots that use AI to interact with customers and identify potential fraudulent activity. For example, a chatbot might ask customers questions about their recent transactions or shipping addresses. The chatbot can flag the transaction for further investigation if it detects any suspicious activity.
  • Fraud analytics platforms that use AI to analyze large amounts of data to identify patterns and trends that might indicate fraudulent activity. For example, a fraud analytics platform might identify a group of customers who are all making small, high-frequency transactions in different countries—a potential sign of money laundering.

Machine learning is a subset of AI. Machine learning involves the application of statistical techniques and modeling to create algorithms that improve with experience. So, instead of explicitly programming rules, ML algorithms learn and adapt from the data they process, making autonomous predictions or decisions without being specifically programmed to do so.

Here’s what fraud detection using machine learning could look like: 

  • Machine learning fraud detection systems use ML algorithms to identify fraudulent transactions. Machine learning algorithms can learn to identify patterns and anomalies in data that might indicate fraud, even if those patterns are not obvious to humans.
  • Machine learning fraud scoring models use ML algorithms to assign risk scores to transactions. The risk scores are then used to flag transactions for further investigation.
  • Machine learning fraud analytics platforms use ML algorithms to analyze large amounts of data to identify patterns and trends that might indicate fraudulent activity.

AI and Machine learning are powerful tools that can be used to prevent fraud—especially compared to traditional fraud detection and prevention methods. 

How does ML compare to traditional fraud detection methods?

Before the advent of AI, financial institutions and businesses would use rule-based systems to identify fraudulent transactions. For instance, a rule-based system might flag a transaction as fraudulent if the customer used an IP address from a different country from their billing address or made a large transaction outside of normal business hours. 

The only way to flag these rule violations was manually, meaning this process was time-consuming, labor-intensive, and error-prone. Professionals would assign a risk score to each transaction based on the customer’s account history, the amount of the transaction, and when the transaction was made. Any transactions receiving a risk score above a certain threshold were flagged for further review. 

Not only was this process for fraud detection unwieldy, but it was also inefficient and inaccurate. Using lots of rules tended to generate false positives, blocking real customers from using their accounts to do business and increasing customer service costs. Rules-based manual monitoring also makes it difficult to account for changing fraudulent behavior. Fraud schemes evolve and get more sophisticated over time and detection rules can quickly fall out of date. 

And, as the business grows, it becomes more difficult to evaluate each and every transaction. The rules-only approach means that your fraud detection team must constantly add more rules as fraud evolves. That puts a burden on the fraud analysis team as well as the system that’s used to catalog transactions. 

Machine learning and AI can overcome these challenges by learning to identify patterns and anomalies in data that might indicate fraud, even if those patterns are not obvious to humans. These algorithms can be scaled to handle large amounts of data, making them more cost-effective than a rules-based system. 

How to use machine learning for fraud detection

There are a number of ways to implement machine learning for fraud detection and prevention. Here’s how machine learning can be used to lower the risk of fraudulent transactions hitting your bottom line. 

1. Gather data and train the model 

First, a machine learning model must be trained with data to understand the types of transactions that your business processes. ​​ solutions, for example, combine your data with our powerful custom Machine Learning Models to train the tools to recognize potential fraud. Understand the most powerful factors driving your model’s risk scores so you can make more informed decisions and take the right next step.

2. Fraud scoring and analytics

Machine learning algorithms can assign a risk score to every transaction.’s machine learning tools quantify the relative risk of fraud for every event on a scale of 1 to 99 and translate it into action. Transactions that receive a “high” risk score are flagged for further investigation. 

Machine learning can also streamline the review workflow.’s platform offers an easy way to auto-approve low-risk activities while auto-canceling the riskiest ones to reduce the number of cases your fraud analysts manually review.

In addition, machine learning algorithms can analyze large amounts of data to identify patterns and trends that might indicate fraud.’s analytics platform guards against model degradation and drift as customer patterns evolve & market conditions change. 

3. Fraud prevention 

Fraud prevention tools that use machine learning algorithms can identify fraudulent transactions in real-time and stop fraud before it hits your books. Using big data, Machine learning can automatically detect fraudulent activity. For instance, anomaly detection, such as device intelligence, can identify when a malicious bot, fraudster, or other bad actor is present on your site. 

[Read more: Big Data Analytics: A Fraud Prevention Game-Changer

Getting started with machine learning for fraud detection

Integrating machine learning for fraud detection doesn’t happen overnight. The first step is to audit your fraud risk and understand the different types of fraud your organization is vulnerable to. From there, you’ll need to gather and clean the data required to train the machine-learning model. 

There are dozens of tools that offer various applications of machine learning for fraud detection and prevention, many of which use different algorithms. There is no single “best fraud detection machine learning algorithm.” Some tools use logistic regression, decision trees, random forests, or k-means to detect fraud. Others use a combination of AI and machine learning. The best algorithm for a particular application will depend on the type of data and the desired accuracy. offers a holistic approach to fraud detection and prevention. Organizations are able to reduce fraud by 80% with more accurate detection using our platform. 

“We wanted to make sure we were using all the tools available in the industry, so we made the jump to’s machine learning. There’s just a lot of data that a computer can analyze that my clients can’t,” said one user can also help mitigate risk by identifying and addressing the root causes of fraud. For example, if fraud is occurring because customers are reusing passwords, big data analytics can be used to identify these customers and require them to change their passwords. offers an award-winning fraud prevention platform to help digital businesses quickly detect transactional anomalies and pinpoint fraud using artificial intelligence, big data, and live-streaming visualizations. Learn more about how to use machine learning for fraud detection, and sign up for a free demo to get started.