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.

Big data, the Internet of Things, and social media have all forever changed the art and science of marketing and that's already old news. To even remain relevant in the near future, marketers need to provide personalized experiences at the speed and scale only automation can fulfill. Artificial Intelligence for Marketing provides marketers with a comprehensive introduction to the field of data science without requiring a background in advanced programming and mathematics. Today's marketing decision-making happens in real time, every day, and artificial intelligence (AI) and machine learning (ML) give marketers a hands-free way to quickly and effectively respond to data from a customer or potential buyer and tailor fit a product and buying experience. Buyers on the fence can automatically receive special incentives to buy, and other customers can be directed to related products and services based on data-driven insight.