Modernizing fraud prevention with machine learning

The number of digital transactions has skyrocketed. As consumers continue to spend and interact online, they have growing expectations for security and identity verification. As fraudsters become savvier and more opportunistic, there’s an increased need for businesses to protect customers from fraud while still providing a seamless online experience.

At the same time, businesses have the ability to access more insights and data than ever before, but may not be leveraging the most effective technology solutions to accurately identify and authenticate consumers online.

machine learning fraud prevention

Fraud concerns and security expectations continue to increase

Uncertain economic conditions and what feels like a barrage of new scams has made consumers and businesses more concerned about online fraud.

Experian’s 2023 U.S. Identity and Fraud Report found that over half of consumers feel like they are more of a fraud target than they were just one year ago. In addition, half of businesses report a high level of concern about fraud risk.

The report found that people worry most about identity theft (64%), stolen credit card information (61%) and online privacy (60%). On the other hand, businesses are concerned about authorized push payments fraud (40%) and transactional payment fraud (34%). Additionally, nearly 70% of businesses said that fraud losses have increased in recent years and most businesses reported that they plan to increase their fraud management budgets by at least 8% to as much as 19%.

Despite their plans to increase their fraud prevention budgets, data shows that businesses may not be completely aligned with consumer expectations.

For example, 85% of people report physical biometrics, such as facial recognition and fingerprints, as the authentication method that makes them feel most secure. However, that identity authentication method is currently used by just a third of businesses to detect and protect against fraud, showing there is still a disconnect between consumer preferences and what businesses are offering.

Finally, consumers not only stress the importance of better security, but they expect their online experiences to be frictionless. This is evident in the data – while 51% considered abandoning a new account opening because of a negative experience, 37% said a bad experience caused them to take their business elsewhere. It’s crucial for businesses to implement fraud solutions that are capable of properly verifying real customers while identifying and treating fraud and providing a positive experience.

Machine learning is necessary for fraud prevention

Businesses understand the need to incorporate machine learning into their anti-fraud strategies.

The main benefits of incorporating machine learning into fraud management is that it can:

  • Enable real-time fraud detection: Machine learning can help businesses detect and prevent fraud threats in real time, helping to identify both known and unknown threats to stay ahead of fraudsters. It can also spot abnormalities that may be hard to catch when doing these processes manually.
  • Analyze large transactions: Machine learning allows businesses to analyze a large quantity of transactions and data sets automatically, extending fraud prevention measures across the entire customer portfolio. This helps identify new and existing fraud risks quickly. It also ensures that legitimate customers can continue transacting with the business without friction.
  • Help evolve strategies by learning with time and experience: Another major benefit of machine learning is that it’s continually learning from previous transactions and new fraud patterns. This means that businesses that incorporate machine learning into their fraud prevention approach now will reap the benefits as more data is incorporated into the solution for faster, better results.

A multilayered approach to fraud that leverages data, machine learning and advanced analytics is crucial for businesses trying to stay ahead of fraud trends. Machine learning modernizes identification and fraud prevention, allowing businesses to fight new and old forms of fraud as they occur while providing their customers with a seamless, positive experience.

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