The continually changing fraud environment means that there are increasingly new ways of committing fraud, so organizations need something more than a reactive approach.

Fremont, CA: Cybersecurity is a broad subject that covers several problems and weaknesses. No part of the digital world is left untouched, from stealing customer information through data breaches to manipulating significant elections.

It is also linked to each of these particular vulnerabilities. It sends a message that such actions pay off and encourages fraudsters to push the limits and see how far their illegal activities can go whenever a cybercriminal can hack an account, leak confidential data, or steal information for personal gain.

Here two critical challenges for adopting advanced AI fraud detection technology:

New Entrance of Traditional Businesses in the Online Space

Supervised machine learning can only deliver fraud detection efforts so far in the face of continuously changing variables. It learns according to data training, and data training can only occur when elements of fraud are identified. The continually evolving fraud environment means that there are increasingly new ways of committing fraud, so organizations need something more than a reactive approach.

Unsupervised machine learning (UML) does not rely on lagging indicators and historical data training. Instead, it examines all available data in real-time to identify trends and patterns that bypass conventional fraud detection initiatives. UML can thus detect organized crime rings with greater precision and less false positives and enable businesses to make large-scale decisions before fraud can occur.

Lack of Data Infrastructure to Support Machine Learning

Many organizations wonder if they need to start with an AI machine learning solution that recognizes the risks associated with online fraud or if they will be better off implementing gradual solutions first, thinking that machine learning is too advanced for their current state.