Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection (Wiley and SAS Business Series)
| Author | : | |
| Rating | : | 4.49 (672 Votes) |
| Asin | : | B012WA66SK |
| Format Type | : | |
| Number of Pages | : | 259 Pages |
| Publish Date | : | 2015-04-05 |
| Language | : | English |
DESCRIPTION:
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 analytic
Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.. 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.Examine fraud patterns in historical dataUtilize labeled, unlabeled, and networked dataDetect fraud before the damage cascadesReduce losses, increase recovery, and tighten securityThe longer fraud is allowed to go on, the more harm it causes. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. Fraud prevention relie
Professor Bart Baesens is a professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on big data & analytics, customer relationship management, web analytics, fraud detection, and credit risk management.His findings have been published in well-known international journals (e.g. Machine Learning, Management Science, IEEE Transactions on Neural
Shallow Coverage of Broad Areas of Advanced Knowledge - Not for the Uninitiated Lex Talionis I've written reviews for several books on Amazon, and not until I reviewed this book were any of my reviews ever rejected. This is my second attempt to review this book.Contrary to the review by Gerard Meester (who from his dearth profile appears may have an affiliation with one or more of the authors), this is far from "the best book" available to practitioners in fraud detection and prevention using Big Data. The best book in this area is hands down Financial Forensics Body of Knowledge (W. Matthew B. said Five Stars. Great book - very detailed in the applications of fraud analytics.. Five Stars Great book!