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Product details
File Size: 16636 KB
Print Length: 400 pages
Page Numbers Source ISBN: 1119133122
Publisher: Wiley; 1 edition (July 27, 2015)
Publication Date: July 27, 2015
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B012WA66SK
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Amazon Best Sellers Rank:
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Doesn’t tell you what to do
This book has a good information.ThanksEnrique
Several good ideas, but a bit heavy on the implementation specific math formulas for me. Still I enjoyed it and found it useful.
It's a very good book. This book met my expectations.
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 (Wiley Finance), which covers hundreds of analytical techniques for fraud detection in a manner understandable to most people with a modicum of educationTo its credit, the book does cover some rather esoteric statistical fraud detection methods not covered in other texts, but it provides only brief coverage of these advanced statistical techniques, apparently for those already learned in the data sciences.To its detriment, the book does not provide a clear presentation of the application of the advanced formulae in a manner that is understandable to the uninitiated. It would be very nice to see the authors provide this material in a manner that is more detailed so that the reader can work through the methods without the need to resort to the plethora of references at the end of each chapter in order to gain an understanding of the material.
Great book - very detailed in the applications of fraud analytics.
Great book!
This book summarizes some standard statistical techniques (clustering, regression, SVMs, ensemble methods etc) and tries to tie them back to fraud analytics. However I feel that it didn't really succeed. People who are already familiar with the statistical techniques will find the insights into fraud analytics somewhat lacking. Those who don't know the statistical techniques would be better off to study them elsewhere using standard machine learning or statistical learning textbooks. There are a great many books that explain the exact same techniques in a superior manner. Don't think there are lots of good datasets in the book either, nearly all the data is just made up. Nor is there much in the way of hands on tutorials. Sadly this is quite a common problem, people try to write a book about "statistics" for X. But end up just summarizing a lot of statistical techniques in a manner inferior to other books, and ultimately have relatively little to say about X. I recommend that readers just bypass this book, maybe there is a good book on fraud analytics that assumes the reader already knows statistics and can deliver insights more quickly, but I haven't found it yet. This book is 95% summarizing standard stats techniques and a few obvious points about fraud analytics.
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