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Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques

Baesens Verbeke

20 valoraciones por Goodreads
ISBN 10: 1119133122 / ISBN 13: 9781119133124
Nuevos Condición: New Encuadernación de tapa blanda
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Librería en AbeBooks desde: 2 de octubre de 2008

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Opt EXPEDITED shipping for 2 to 4 day delivery - Brand NEW - International Edition - 1ed - Harbound Cover, SAME Contents as in US edition - SHRINKwrapped BOXpacked - There is no CD or Access Code, unless specified above - Ships from various locations. N° de ref. de la librería DN5

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Detalles bibliográficos

Título: Fraud Analytics Using Descriptive, ...

Encuadernación: Soft cover

Condición del libro:New

Edición: 1st Edition, International Edition

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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 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. It 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. * Examine fraud patterns in historical data * Utilize labeled, unlabeled, and networked data * Detect fraud before the damage cascades * Reduce losses, increase recovery, and tighten security The 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. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.

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