Telecom industries lose globally 46.3 Billion USD due to fraud. Data mining and machine learning techniques (apart from rules oriented approach) have been used in past, but efficiency has been low as fraud pattern changes very rapidly. This paper presents an industrialized solution approach with self adaptive data mining technique and application of big data technologies to detect fraud and discover novel fraud patterns in accurate, efficient and cost effective manner. Solution has been successfully demonstrated to detect International Revenue Share Fraud with <5% false positive. More than 1 Terra Bytes of Call Detail Record from a reputed wholesale carrier and overseas telecom transit carrier has been used to conduct this study.

翻译：全球电信行业因欺诈行为每年损失463亿美元。过去虽已采用数据挖掘与机器学习技术（除规则导向方法外），但由于欺诈模式变化极为迅速，其效率始终较低。本文提出一种工业化解决方案，采用自适应数据挖掘技术并应用大数据技术，以精准、高效且经济的方式检测欺诈行为并发现新型欺诈模式。该方案已成功应用于国际收入分成欺诈检测，误报率低于5%。本研究使用了来自一家知名批发运营商及海外电信转接运营商超过1TB的通话详细记录数据。