The Future Is Now: Real-Time Fraud Detection Is a Reality

For decades, a lack of timeliness has plagued fraud detection efforts. The reason for this is twofold: 

• First, traditional fraud tools can only be trained to detect known fraud. When criminals discover new ways to evade these tools, then fraud teams must retrain their tools to accommodate for new gaps in defenses. This results in a never-ending game of catchup, where fraud evolves first and companies are left trying to prevent it from occurring in the future. 
• Second, data that exists across disparate systems make it difficult to capture all necessary insights to detect fraud. Without a unified platform to capture and analyze data, fraud that occurs on multiple channels may go undetected.

Today, there’s DataVisor, a comprehensive fraud detection platform that analyzes multiple behaviors and actions and provides real-time insights holistically across all channels. 

The Growing Need for Real-Time Fraud Detection

Fraud rings continue to grow in complexity and scale. Data from Aite Group’s Fraud and AML report notes that there’s been an 84% increase in the number of breached data reports, along with record-setting malicious login attempts during the COVID-19 pandemic. Currently, financial credentials for online banking are selling for just $35 on the dark web, while credit card numbers can be purchased for as little as $12. 

What’s more, many large-scale crime rings operate much like legitimate businesses. They’re actively recruiting specialists in AI and machine learning and using that expertise to hack systems. It’s becoming harder for companies to separate valid customers from synthetic identities and bot attacks, leaving companies to make one of two classic choices: either increase their fraud detection efforts and potentially create more friction for good customers, or accept a certain amount of fraud as a cost of doing business.

A Better, Smarter Way Forward

With DataVisor’s real-time fraud detection platform, now there’s a desirable third option. By putting unsupervised machine learning (UML) to work along with more traditional fraud detection tools, companies can evolve beyond known fraud threats and improve their efforts in stopping fraud in its tracks. 

Using AI and machine learning, extensive data retraining just to keep up with evolving fraud attacks will become a thing of the past. Unlike rules engines, unsupervised machine learning tools don’t rely on historic data to develop insights and make decisions, and they can alert companies of suspicious behaviors before transactions are completed.

This is especially important as the window of time between initiating fraud (e.g. creating a fake account) and completing a transaction is shortening. Fraudsters are striking quicker than ever, but real-time fraud detection enables prevention in the moment.  

Stopping Fraud at the Gate with DataVisor

Datavisor’s forward-thinking work in unsupervised machine learning is leading the charge in real-time fraud detection. No longer blue-sky thinking, UML works along with the rest of DataVisor’s comprehensive suite of fraud detection tools to examine multiple data points across all channels in real time to identify and prevent fraud with great accuracy.