Top 2 Challenges for Adoption of AI Fraud Detection Technology

Cybersecurity is a broad topic that encompasses a multitude of issues and vulnerabilities. From stealing consumer details through data breaches to infiltrating major elections, no area of the digital world is left unscathed. 

Each of these unique vulnerabilities is also interconnected. Any time a cybercriminal can hack an account, leak sensitive information, or steal information for personal gain, it sends a message that such activities pay off and encourages fraudsters to test the boundaries to see how far their illicit activities can go.

If you conduct business online, it’s important to understand your responsibility — to your organization, your customers, and your stakeholders — to mitigate digital fraud. 

AI is being increasingly developed to detect and prevent fraud, ideally before fraudulent activities occur. However, two key challenges prevent many companies from adopting advanced AI fraud detection technology:

1. A Lack of Data Infrastructure to Support Machine Learning

Major digital players like Google and Facebook have made Big Data a top priority, and they have the infrastructure to do so. This isn’t the case for Main Street USA, however. Many small to mid-sized businesses that are just starting to grow their online presence aren’t fully aware of the threats they could face online and may even believe they are too small to pop up on a cyber criminal’s radar.

Granted, these businesses likely collect data on their customers, website traffic, and social media engagement. But they may not have the data infrastructure needed to evaluate user activities and behaviors to create a baseline understanding of what fraud looks like. Since AI and machine learning works by “learning” from data, a lack of data to feed the system can impede the learning curve, especially in the case of supervised machine learning. 

For those businesses that do understand the risks associated with online fraud, many wonder if they really need to start with an AI machine learning solution or if they’d be better off to introduce incremental solutions first, assuming (incorrectly) that machine learning is too technologically advanced for their current state.

2. The Relatively New Entrance of Traditional Businesses in the Online Space

For many years, traditional businesses have had fraud prevention and detection policies in place, but those protections were not designed for the digital world. With the acceleration of digital transformation across a broad swath of industries and the increasingly digital nature of customer interaction, traditional businesses are scrambling to find their footing in a new world, one with rapidly evolving threats and challenges.

Online fraud is sophisticated, complex, and ever-evolving. Thus, it calls for a proactive rather than a reactive response. For traditional businesses dipping their toes into the online space for the first time, that necessitates a shift in mindset and method.

Meeting Fraud Detection Adoption Challenges with Unsupervised Machine Learning

In the face of constantly changing variables, supervised machine learning can only take fraud detection efforts so far. It learns based on data training, and data training can only occur when fraud elements are known. The quickly-evolving fraud landscape means new ways of conducting fraud are continually occurring, so businesses need something more than a reactive approach.

Unsupervised machine learning (UML) doesn’t rely on historic data training and lagging indicators. Instead, it reviews all available data in real time to detect trends and patterns that evade traditional fraud detection measures. In doing so, UML can identify organized crime rings with greater accuracy and fewer false positives and allow companies to make bulk decisions before fraud can occur. 

DataVisor uses both supervised and unsupervised machine learning to provide comprehensive fraud prevention and detection services to its clients. Talk with our expert to discover more about how DataVisor is preparing companies for the future of digital fraud and risk.

Schedule a quick demo to talk with DataVisor’s fraud experts on how DataVisor is helping to combat digital channel fraud.