Modernizing fraud prevention with machine learning

The number of digital transactions has exploded. As consumers continue to spend and interact online, they have increasing expectations around security and identity verification. As fraudsters become savvier and more opportunistic, businesses increasingly need to protect their customers from fraud while providing a seamless online experience.

At the same time, businesses have the opportunity to access more information 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 rise

Uncertain economic conditions and what appears to be a barrage of new scams have made consumers and businesses more concerned about online fraud.

Experian’s 2023 U.S. Identity and Fraud Report Finds More Than Half of Consumers Feel Like They’re More of a Target for Fraud Than They Did Just a Year Ago . Additionally, half of businesses say they are very concerned about the risk of fraud.

The report found that people are most worried about identity theft (64%), theft of credit card information (61%), and online privacy (60%). In contrast, businesses are concerned about authorized push payment fraud (40%) and transactional payment fraud (34%). Additionally, nearly 70% of companies said fraud losses have increased in recent years and most companies said they plan to increase their fraud management budgets by at least 8%. up to 19%.

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

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

Finally, consumers not only emphasize the importance of better security, but they also expect their online experiences to be seamless. This is clear from the data: while 51% of respondents considered giving up on opening a new account due to a negative experience, 37% said a bad experience caused them to take their business elsewhere. It is crucial for businesses to implement anti-fraud solutions that can properly verify real customers while identifying and addressing fraud and providing a positive experience.

Machine learning is necessary for fraud prevention

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

The main benefits of integrating machine learning into fraud management are:

  • Enable real-time fraud detection: Machine learning can help businesses detect and prevent fraud threats in real time, helping them identify known and unknown threats to stay one step ahead of fraudsters. It can also detect anomalies that may be difficult to detect when performing these processes manually.
  • Analyze important transactions: Machine learning allows businesses to automatically analyze a large amount of transactions and data sets, extending fraud prevention measures across the entire customer portfolio. This helps to quickly identify new and existing fraud risks. This also ensures that legitimate customers can continue to transact with the company without friction.
  • Help evolve strategies by learning with time and experience: Another major advantage of machine learning is that it continually learns from previous transactions and new fraud patterns. This means that businesses that integrate machine learning into their fraud prevention approach will now reap the benefits as more data is incorporated into the solution for faster and better results.

A multi-layered 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 fraud identification and prevention, enabling businesses to combat new and old forms of fraud as they arise while providing their customers with a seamless and positive experience.

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