The rise of big data along with new analytical techniques represent new opportunities to put data to work for a wide range of uses. At WeDo Technologies, we’ve developed our software to leverage data analytics with large datasets in order to detect telecom fraud.
Big Link Analysis is a new fraud detection technique developed by WeDo Technologies which uses a combination of social network-based analysis and OSS/BSS information to stop subscription fraud.
The benefits of social media are unprecedented: minute communication, the maximization of reach in advertisement and voice, and the passive acquisition of information about people, cultures, places, and events by just the mere action of scrolling down a user timeline.
To illustrate this fraud detection technique, let’s look at a real-life application of network analysis for subscription fraud detection. The data under consideration comes from using social logins to accelerate the process users take to subscribe to OTT services.
In the case of identity authentication, CSPs must re-validate the authenticity of an identity provided. In the example previously shown, you can see how OTT providers allow customers to log in using their social media accounts, such as Facebook or Google, as a convenient way for customers to sign up for new services. For the CSPs who are delivering billing capabilities by accepting social information to subscribe services, should find new tactics to identify synthetic or stolen identities as part of the risk management strategy. While social media information may provide intel on your customers’ friends and the TV shows they like, how do you know that it is actually a real person, and not a ‘bot’ claiming that identity?
Most synthetic or stolen identities use elements of a real person's identity to construct a whole other person. By combining evidence from two different sources: Social data for subscriber identity validation and OSS/BSS information for a 360-degree view of the customer information, WeDo Technologies’ Digital Footprint takes you a step further in detecting subscription fraud before it even occurs.
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