Fraud Analytics Using Descriptive, Predictive, And Social Network Techniques - A Guide To Data Sc...

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[b]Fraud Analytics Using Descriptive, Predictive, And Social Network Techniques - A Guide To Data Science For Fraud | 15.74 MB[/b]
[b]English | 382 Pages[/b]
[b]Title:[/b] Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques
[b]Author:[/b] Bart Baesens, Véronique Van Vlasselaer, Wouter Verbeke
[b]Year:[/b] 2015

[b]Description:[/b]
[b]Detect fraud earlier to mitigate loss and prevent cascading damage[/b] [i]Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques [/i]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...

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