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Fraud Analytics Using Descriptive, Predictive, and Social ~ Library of Congress Cataloging-in-Publication Data: Baesens, Bart. Fraud analytics using descriptive, predictive, and social network techniques : a guide to data science for fraud detection / Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke. pages cm. — (Wiley & SAS business series) Includes bibliographical references and index.

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Predictive Analytics for Fraud Detection - Fraud Analytics ~ Different types of predictive analytics techniques have been developed in the literature originating from a variety of different disciplines such as statistics, machine learning, artificial intelligence, pattern recognition, and data mining. The distinction between those disciplines is getting more and more blurred and is actually not that .

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Fraud Detection Using Descriptive, Predictive, and Social ~ A typical organization loses an estimated 5 of its yearly revenue to fraud. This course shows how learning fraud patterns from historical data can be used to fight fraud. The course discusses the use of supervised learning (using a labeled data set), unsupervised learning (using an unlabeled data set), and social network learning (using a networked data set).

Fraud: Detection, Prevention, and Analytics! - Fraud ~ In this first chapter we set the scene for what's ahead by introducing fraud analytics using descriptive, predictive, and social network techniques. We start off by defining and characterizing fraud and discuss different types of fraud.

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Fraud Analytics: Post‐Processing - Fraud Analytics Using ~ The result of the analytics step is an analytical fraud model built using a descriptive, predictive, or social network or combined technique. Essentially, the analytical model reduces to a mathematic.

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Fraud Analytics Using Descriptive, Predictive, and Social ~ Book Description. Detect fraud earlier to mitigate loss and prevent cascading damage. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is 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 .

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Using analytics to predict fraud / SAS ~ Semi-supervised techniques, where some business knowledge can direct the analyses. Unsupervised techniques, such as segmentation, which are exploratory. Fighting fraud with predictive analytics. By creating a predictive model, predictive analytics uses a specific set of data that contains known outcomes for a particular target.

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Fraud Analytics Using Descriptive Predictive And Social ~ Fraud Analytics Using Descriptive Predictive And Social Network Techniques A Guide To Data Science For Fraud Detection Wiley And Sas Business Series Author: dc-75c7d428c907.tecadmin-2020-10-19T00:00:00+00:01 Subject