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.

Despite the amount of data that businesses collect, many of these are still hidden within the old-fashioned ways of analysis. Fraud Analytics is a process that involves gathering and storing vast amounts of data. This allows researchers to analyze the data to identify patterns and anomalies, as well as uncover hidden threats. Over the past decade, the demand for fraud detection has grown significantly in various industries. This is due to the increasing number of organizations wanting to identify fraud activities in their organizations.

The use of artificial intelligence and machine learning in bank fraud analytics is continuing to move from reactively mitigating fraud that's already occurred to preventing fraudulent activities from actually happening--but in ways that try not to block legitimate customer transactions. As anti-fraud technology has become more advanced and scalable, some banks are now investing in a cross-product, omnichannel view of customer behavior, says Philippe Guiral, who leads Accenture's North America fraud and financial crime practice. This means leveraging customer data across domains within the organization to gain more insights of customer behavior to better assess whether any particular transaction is suspicious. A growing number of banks are now building cases to show these solutions can not only improve fraud prevention rates, but also enhance the customer experience and be applied across additional functions--including financial crime, 'Know Your Customer,' risk and customer intelligence--to uncover hidden risks and discover new opportunities, he says. Indeed, it's critical to have a strong fraud analytics solution that can give banks a comprehensive view of a customer's identity and real-time insights into application activity, says Kimberly White, senior director of fraud & identity at LexisNexis Risk Solutions in Alpharetta, Georgia.

The sooner fraud detection occurs the better as the likelihood of further losses is lower, potential recoveries are higher, and security issues can be addressed more rapidly. Catching fraud in an early stage, though, is more difficult than detecting it later, and requires specific techniques. Packed with numerous real-world examples, Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques authoritatively shows you how to put historical data to work against fraud. Authors Bart Baesens, Véronique Van Vlasselaer, and Wouter Verbeke expertly discuss the use of unsupervised learning, supervised learning, and social network learning using techniques across a wide variety of fraud applications, such as insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, and tax evasion. This book provides the essential guidance you need to examine fraud patterns from historical data in order to detect fraud early in the process.

Fraud analytics is an endless game of cat and mouse, but machine learning just might be the tool to help fraud professionals win this game. In the financial services world, fraudsters must be faster and smarter than the slowest bank to be "quids in". And a bank must be better than the fraudster to avoid being a victim. Analytics and data science play a pivotal role in this. However, a troubling issue that banks often face is the bridge between the data scientist and the fraud analyst: one really understands statistics while the other understands fraud.

The E-learning course starts by refreshing the basic concepts of the analytics process model: data preprocessing, analytics and post processing. We then discuss decision trees and ensemble methods (bagging, boosting, random forests), neural networks, support vector machines (SVMs), Bayesian networks, survival analysis, social networks, monitoring and backtesting analytical models. Throughout the course, we extensively refer to our industry and research experience. The E-learning course consists of more than 20 hours of movies, each 5 minutes on average. Quizzes are included to facilitate the understanding of the material.