Key Takeaways
- A new study found that 68% of business leaders believe that their current security technology is inadequate in dealing with attacks has
- Fortunately, businesses are seeing great benefits from adopting machine learning into their security systems.
- Machine learning systems have the ability to be continually retrained and refined to suit the rapidly evolving security threat landscape in 2026.
According to a new report from Experian and Forrester Consulting, 68% of business leaders believe that their current security tools aren’t up to the task when it comes to fending off cyber attacks.
However, as threats evolve in 2026, many businesses are turning to machine learning to close the gap, and the results have been quite promising.
The main benefit businesses are seeing is real-time fraud detection, as well as the ability to provide better customer service and improved accuracy when it comes to detecting threats.
Leaders Aren’t Confident in Security Tools
A new report from Experian and Forrester Consulting has revealed that 68% of business leaders believe their current security tools can’t keep up the pace when it comes to navigating the evolving threat of fraud attacks.
This remains a huge concern for businesses, particularly as 67% expect more fraud attacks in 2026 than in the previous year.
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Businesses Are Turning to Machine Learning for Security
According to the report, businesses are investing in machine learning as a way to bolster security in 2026.
71% of businesses are investing more in technology rather than human analysts, suggesting that the potential limitations of traditional methods are becoming more obvious.
In particular, businesses are turning to machine learning to help process the large datasets being produced for more accurate fraud detection measures. And, they’re seeing great benefits, with 70% of respondents agreeing that machine learning has improved their ability to detect fraud that a rules-only system would have missed.
Likewise, 66% of businesses say that machine learning has improved their ability to identify new types of fraud faster than before. This positions machine learning as a key element of security protocols for the future, as threats develop more with AI.
Benefits of Machine Learning in Combatting Fraud Attacks
The main benefit businesses identified in using machine learning to tackle fraud is through real-time detection measures, which was cited by 54% of respondents. Models can identify and flag anything suspicious much faster than a traditional system can.
Similarly, 52% of respondents say that better customer experience with less security friction is another key benefit. Machine learning allows businesses to make faster security decisions, and it allows them to carry out passive fraud checks regularly.
51% of respondents also noted an improved accuracy in their detection of fraud when adopting machine learning, especially when compared with rules-based systems alone. As models can be continually retrained and improved, businesses can be sure that the system won’t miss any new threats.
When adopting machine learning into a security stack, the report claims the most important element is having sufficient, high-quality data available, in order for the system to be as effective as possible.
