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.

电信行业因欺诈而在全球范围内损失了 463 亿美元。本文提出了一种工业化的解决方案，采用自适应数据挖掘技术和大数据技术来准确、高效和具有成本效益地检测欺诈，并发现新的欺诈模式。该解决方案已成功地检测出少于 5% 的虚假阳性的国际收入分成欺诈。本研究使用了一家有声望的批发运营商和海外电信中转运营商超过 1TB 的通话详单记录。