The typical approach to combating fraud is to look at all the different ways fraudsters operate and find some indicator based on their objectives. But every time one fraud tactic is identified, the fraudster evolves its tactics to evade detection. Increasingly, fraud resembles valid traffic. This article originally appeared on the TrafficGuard blog. Every time the fraudster finds a new vulnerability, valuable time is lost in defining the tactic and finding the rule to stop it.
Machine learning has become an invaluable tool in the fight against fraud. It combines computational statistics, artificial intelligence, signal processing, optimisation, and other methods to identify patterns. Machine learning has been a significant breakthrough in helping companies move from reactive to predictive by highlighting suspicious attributes or relationships that may be invisible to the naked eye but indicate a larger pattern of fraud. The great value of machine learning is the sheer volume of data that computers can analyse that humans cannot, thanks to a variety of pattern recognition algorithms. With this you can add exponentially more data to your analysis -- but selecting the right data and approach to model the problems is critical.
How do you consistently hit a moving target? That's the challenge facing fraud prevention teams as criminals set the sights on credit cards, current accounts, loans and other financial products. Experian's Hunter fraud statistics show that younger people – particularly millennials, many of whom live in blocks of apartments – continue to be the most common victims for fraudsters, yet criminals are also looking elsewhere. Generally, we are seeing fraud against older people rising. Rural dwelling, wealthier homeowners, who make lengthy commutes to work or are retired, experienced nearly a 30% increase in fraud last year.
DataVisor, the leading fraud detection company with solutions powered by transformational AI technology, announced the availability of dEdge, an anti-fraud solution that detects malicious devices in real-time, empowering organizations to uncover known and unknown attacks early, and take action with confidence. "Most consumer-facing organizations today provide their customers opportunities to interact with the business through an online channel. Even traditional industries like banking enable customers to bank through mobile applications. To validate the authenticity of this interaction, data needs to be collected and analyzed at the source" With growing adoption of mobile devices and the emergence of the always-on economy, by many measures, when organizations realize that they have been subject to a cyber-attack, it is already too late. Modern fraud detection and prevention require a transformational approach, one that represents a shift back to an earlier point along the timeline of a fraud attack.