Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection (Wiley and SAS Business Series) - Tapa dura

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BART BAESENS is a full professor at KU Leuven, and a lecturer at the University of Southampton. He has done extensive research on analytics, customer relationship management, web analytics, fraud detection, and credit risk management. He regularly advises and provides consulting support to international firms with respect to their analytics and credit risk management strategy.

VÉRONIQUE VAN VLASSELAER is a PhD researcher in the Department of Decision Sciences and Information Management at KU Leuven. Her research focuses on the development of new techniques for fraud detection by combining predictive and network analytics.

WOUTER VERBEKE is an assistant professor at Vrije Universiteit Brussel (Brussels, Belgium). His research is situated in the field of predictive analytics and complex network analysis with applications in fraud, marketing, credit risk, human resources management, and mobility.

De la contraportada

THE DEFINITIVE GUIDE TO THE DETECTION AND PREVENTION OF FRAUD THROUGH DATA ANALYTICS

Catch fraud early! Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques shows you how with a thorough overview of how to prevent losses and recover quickly as well as the security issues you need to address now. Exploring how auditors, corporate security prevention managers, and fraud prevention professionals can stay one step ahead of cyber criminals, this book addresses the different types of analytics in detecting fraud, including descriptive analytics, predictive analytics, and social network analysis.

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques offers a current, state-of-the-art detection and prevention methodology, describing the data necessary to detect fraud. Taking you from the basics of fraud detection data analytics, through advanced pattern recognition methodology, to cutting-edge social network analysis and fraud ring detection, this book presents essential coverage of:

• The fraud analytics process model
• Big data
• Break point/peer group analysis
• Anomaly detection
• Linear/logistic regression
• Neural networks
• Ensemble methods
• Social network metrics
• Bipartite graphs
• Community mining
• Visual analytics
• Model monitoring and backtesting

Insightful and clearly written, this hands-on guide reveals what you need to know about fraud analytics and the secret to putting historical data to work in the fight against fraud.

De la solapa interior

The sooner fraud detection occurs the betteras 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.

Providing a clear look at the pivotal role analytics plays in managing fraud, this book includes straightforward guidance on:

• Fraud detection, prevention, and analytics
• Data collection, sampling, and preprocessing
• Descriptive analytics for fraud detection
• Predictive analytics for fraud detection
• Social network analytics for fraud detection
• Post processing of fraud analytics
• Fraud analytics from an economic perspective

Read Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques for a comprehensive overview of fraud detection analytical techniques and implementation guidance for an effective fraud prevention solution that works for your organization.