Fraud Analytics Using Descriptive Predictive and Social Network Techniques Bart Baesens

Fraud Analytics Using Descriptive Predictive and Social Network Techniques download cover ebookTitle: Fraud Analytics Using Descriptive Predictive and Social Network Techniques

Author:Bart Baesens

Genre: Academic > Sociology > Social pathology. Social and public welfare. Criminology > Criminology > Crimes and offensesAcademic > Sociology > Social pathology. Social and public welfare. Criminology > Social service. Social work. Charity organization and practiceAcademic > Sociology > Social pathology. Social and public welfare. Criminology > Life savingComputers > Database Management > Data Mining

The Sinopsys of Fraud Analytics Using Descriptive Predictive and Social Network Techniques

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 specialized techniques than detecting fraud at the 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 , 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. 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 harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes and 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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