Detecting telecommunication fraud by understanding the contents of a call

Abstract

Telecommunication fraud has continuously been causing severe financial loss to telecommunication customers in China for several years. Traditional approaches to detect telecommunication frauds usually rely on constructing a blacklist of fraud telephone numbers. However, attackers can simply evade such detection by changing their numbers, which is very easy to achieve through VoIP (Voice over IP). To solve this problem, we detect telecommunication frauds from the contents of a call instead of simply through the caller’s telephone number. Particularly, we collect descriptions of telecommunication fraud from news reports and social media. We use machine learning algorithms to analyze data and to select the high-quality descriptions from the data collected previously to construct datasets. Then we leverage natural language processing to extract features from the textual data. After that, we build rules to identify similar contents within the same call for further telecommunication fraud detection. To achieve online detection of telecommunication frauds, we develop an Android application which can be installed on a customer’s smartphone. When an incoming fraud call is answered, the application can dynamically analyze the contents of the call in order to identify frauds. Our results show that we can protect customers effectively.