Fraud Analytics Using Machine-learning & Engineering on Big Data (FAME) for Telecom
This addresses a critical financial loss problem for telecom industries, offering an incremental improvement over existing methods.
The paper tackles telecom fraud detection by developing an industrialized solution using self-adaptive data mining and big data technologies, achieving less than 5% false positive rates for International Revenue Share Fraud.
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.