{"product_id":"fraud-analytics-using-descriptive-predictive-and-social-network-techniques-a-guide-to-data-science-for-fraud-detection-hardcover","title":"Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection - Hardcover","description":"\u003cp\u003eby \u003cb\u003eBart Baesens\u003c\/b\u003e (Author), \u003cb\u003eVeronique Van Vlasselaer\u003c\/b\u003e (Author), \u003cb\u003eWouter Verbeke\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDetect fraud earlier to mitigate loss and prevent cascading damage\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eFraud Analytics Using Descriptive, Predictive, and Social Network Techniques \u003c\/i\u003eis an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention.\u003c\/p\u003e \u003cp\u003eIt is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExamine fraud patterns in historical data\u003c\/li\u003e \u003cli\u003eUtilize labeled, unlabeled, and networked data\u003c\/li\u003e \u003cli\u003eDetect fraud before the damage cascades\u003c\/li\u003e \u003cli\u003eReduce losses, increase recovery, and tighten security\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. \u003ci\u003eFraud Analytics Using Descriptive, Predictive, and Social Network Techniques\u003c\/i\u003e helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.\u003c\/p\u003e\u003ch3\u003eFront Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe 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, \u003ci\u003eFraud Analytics Using Descriptive, Predictive, and Social Network Techniques\u003c\/i\u003e authoritatively shows you how to put historical data to work against fraud. \u003c\/p\u003e\u003cp\u003eAuthors 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. \u003c\/p\u003e\u003cp\u003eProviding a clear look at the pivotal role analytics plays in managing fraud, this book includes straightforward guidance on: \u003c\/p\u003e\u003cul\u003e \u003cli\u003e Fraud detection, prevention, and analytics\u003c\/li\u003e \u003cli\u003e Data collection, sampling, and preprocessing\u003c\/li\u003e \u003cli\u003e Descriptive analytics for fraud detection\u003c\/li\u003e \u003cli\u003e Predictive analytics for fraud detection\u003c\/li\u003e \u003cli\u003e Social network analytics for fraud detection\u003c\/li\u003e \u003cli\u003e Post processing of fraud analytics\u003c\/li\u003e \u003cli\u003e Fraud analytics from an economic perspective\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eRead \u003ci\u003eFraud Analytics Using Descriptive, Predictive, and Social Network Techniques\u003c\/i\u003e for a comprehensive overview of fraud detection analytical techniques and implementation guidance for an effective fraud prevention solution that works for your organization.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe 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, \u003ci\u003eFraud Analytics Using Descriptive, Predictive, and Social Network Techniques\u003c\/i\u003e authoritatively shows you how to put historical data to work against fraud.\u003c\/p\u003e \u003cp\u003eAuthors 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. \u003c\/p\u003e\u003cp\u003eProviding a clear look at the pivotal role analytics plays in managing fraud, this book includes straightforward guidance on: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eFraud detection, prevention, and analytics\u003c\/li\u003e \u003cli\u003eData collection, sampling, and preprocessing\u003c\/li\u003e \u003cli\u003eDescriptive analytics for fraud detection\u003c\/li\u003e \u003cli\u003ePredictive analytics for fraud detection\u003c\/li\u003e \u003cli\u003eSocial network analytics for fraud detection\u003c\/li\u003e \u003cli\u003ePost processing of fraud analytics\u003c\/li\u003e \u003cli\u003eFraud analytics from an economic perspective\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eRead \u003ci\u003eFraud Analytics Using Descriptive, Predictive, and Social Network Techniques\u003c\/i\u003e for a comprehensive overview of fraud detection analytical techniques and implementation guidance for an effective fraud prevention solution that works for your organization.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eBART BAESENS\u003c\/b\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVÉRONIQUE VAN VLASSELAER\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eWOUTER VERBEKE\u003c\/b\u003e 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.\u003c\/p\u003e\n        \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 400\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.2 x 9.3 x 6.3 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e August 17, 2015\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":47384122720434,"sku":"9781119133124","price":52.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0770\/3891\/1666\/files\/a6b619eb751ef9be315bd9a3dfcc05e0.webp?v=1777876689","url":"https:\/\/box.dadyminds.org\/products\/fraud-analytics-using-descriptive-predictive-and-social-network-techniques-a-guide-to-data-science-for-fraud-detection-hardcover","provider":"DADYMINDS BOX","version":"1.0","type":"link"}