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Machine Learning for Fraud Detection: How to Use it in 2020

Machine learning models analyze data and spot patterns to catch fake accounts, fraudulent transactions, account takeover, promotion abuse and more.

Posted by Ann Snook on February 12th, 2020

According to Crowe Global, fraud costs the global economy over $5 trillion annually. Economic losses continue to rise every year as fraudsters come up with new ways to cheat the system. Luckily, machine learning offers a promising solution.

Using machine learning for fraud detection makes the process faster, less expensive and more effective. Plus, when human analysts complement the machines by tackling cases with new or complex problems, you’ll detect and prevent more fraud.

 

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What is Machine Learning?

 

Machine learning is defined as “a set of methods and techniques that let computers recognize patterns and trends and generate predictions based on those.” In other words, a machine analyzes lots of data to “learn” about the past and predict behavior in the future.

 

machine learning for fraud detection

Credit: Intellias

 

In the context of machine learning for fraud detection, there are four steps to creating a machine learning model.

  1. Input data.
  2. Add features that describe legitimate and fraudulent behavior are added. Examples of features include transaction location, age of account, payment method and average transaction value.
  3. Launch a training algorithm to test the machine learning model. This is a set of rules, based on the input data, that the model will follow to analyze transactions. After the training is complete, you have a machine learning model designed for your business and its unique operations and data.
  4. Update machine learning models frequently to keep up with new fraud schemes

 

RELATED: Synthetic Identity Theft: A Serious Threat for Financial Institutions

 

How to Use Machine Learning for Fraud Detection

 

Types of Models

 

Machine learning for fraud detection comes in four types.

Supervised learning models rely on their training algorithms to determine if transactions are legitimate or fraudulent. They can only identify fraudulent behavior that was included in their original input data.

Unsupervised learning models learn to detect patterns with little to no data about what behavior is good or bad. They continuously analyze transactions to update themselves.

Semi-supervised learning models use both data that is tagged as legitimate or fraudulent and patterns they discover on their own.

Finally, reinforcement learning models rely on reinforcement from humans to learn if they make the right decisions. A programmer either rewards or penalizes the machine based on how the model identifies a behavior. It is a trial-and-error approach to help the model learn and remember patterns.

 

RELATED: Phishing Spoofing and Whaling: Tips for Keeping Your Company Safe

 

Who Should Use Machine Learning for Fraud Detection

 

Organizations in a wide variety of industries can use machine learning for fraud detection. If your company uses online accounts or receives or makes payments, you can use this technology. Some of the most common industries that use machine learning for fraud detection include:

  • FinTech
  • Banking
  • Online gambling and gaming
  • Healthcare
  • Insurance
  • Ecommerce

 

Not only can this technology help you spot fraudulent transactions, but also fake accounts, account takeovers and promotion abuse. Machine learning models can even learn to flag unpaid cash on delivery transactions.

For example, if a customer plays a prank or refuses to answer the door and doesn’t pay for their order, the model recognizes that the transaction is unpaid. It then learns patterns about who, where and what types of orders this happens for frequently, helping you prevent future occurrences.

Regardless of your industry, create a machine learning model using your own data if possible. Legitimate and fraudulent behavior differ not just between industries, but also from one company to another. For instance, it would be normal for a customer to spend $1,000 in one transaction at an online furniture retailer, but probably not on a coffee shop app.

 

Make sure your fraud investigations are timely, thorough and well-documented. Download our fraud investigation checklist so you never miss a step.

 

Benefits of Machine Learning for Fraud Detection

 

Greater Work Capacity

 

Humans can only do so much in a day. They need breaks and time to sleep. Machine learning models, though, can work 24/7 without getting stressed out, bored or tired. Unlike human analysts, they also learn patterns and never forget them, so their effectiveness isn’t tied to memory or mood.

 

Higher Efficiency

 

Machines can process more information at a faster rate than human analysts. This, in turn, lets you conduct the same amount of work at a much smaller cost. Not only does machine learning reduce the number of billable hours an organization has to pay its employees, but it may also reduce losses due to fraud.

 

Want to learn more about how to use machine learning for fraud detection? Check out our free cheat sheet.

 

More Effective

 

Because they study so much data, machine learning models can discover patterns of fraudulent behavior that might go unnoticed by humans. Trends that are subtle, non-intuitive or seemingly unrelated to a human can easily be caught by a machine.

With new fraud schemes and an ever-growing volume of data to sort through every day, it can be hard for even the most skilled human analyst to keep up. However, machine learning models actually become more precise as they analyze more information.

 

Frees Up Time for Analysts

 

When human workers aren’t bogged down with data analysis, they have more time to action urgent and complex cases. Machine learning helps with insights and reporting, reducing the amount of tedious work for each analyst. In addition, analysts can address strategic work and mistakes faster, helping your organization maintain customers and even grow.


Ann Snook
Ann Snook

Marketing Writer

Ann is a marketing writer at i-Sight Software. She writes about issues related to investigations of fraud, employee misconduct, corporate security, Title IX, ethics & compliance and more.

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