Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection (Wiley and SAS Business Series)

Dr. Baesens has created an amazing reference guide for fraud analytics. I have used this book for not only academic but real-world cases in investigating social network analysis. This book covers key concepts that can be applied to military, law enforcement, financial, government, insurance and other industries. If you have any interest or study in this field I strongly recommend this book. It permanently stays on my desk.

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!

Fraud analytics starts with an introductory chapter on the scale of the fraud problem, and some examples of types of fraud. It also provides an overview of the chapters that are to come. In the UK fraud losses stand at about £73 billion per annum, typically fraud losses are anything up to 5%. There are many types of fraud: credit card fraud, insurance fraud, healthcare fraud, click fraud, identity theft and so forth.There then follows a chapter on data preparation, sampling and preprocessing. This includes some domain related elements such as the importance of the so-called RFM attributes: Recency, Frequency, and Monetary which are the core variables for financial transactions. Also covered are missing values and data quality which are more general issues in statistics.The core of the book is three long chapters on descriptive statistics, predictive analysis and social networks.The book finishes with chapters on fraud analytics in operation, and a wider view. How do you use these models in production? When do you update them? How do you update them? The wider view includes some discussion of data anonymisation prior to handing it over to data scientists. This is an important area, data protection regulations across the EU are tightening up, breaches of personal data can have serious consequences for those companies involved. Anonymisation may also provide some protection against producing biased models i.e those that discriminate unfairly against people on the basis of race, gender and economic circumstances. Although this area should attract more active concern.A topic not covered but mentioned a couple of times is natural language processing, for example analysing the text of claims against insurance policies.It is best to think of this book as a guide to various topics in statistics and data science as applied to the analysis of fraud. The coverage is more in the line of an overview, rather than an in depth implementation guide. It is pitched at the level of the practitioner rather than the non-expert manager. Aside from some comments at the end on label-based security access control (relating to SQL) and some screenshots from SAS products it is technology agnostic.Occasionally the English in this book slips from being fully idiomatic, it is still fully comprehensible – it simply reads a little oddly. Not a fun read but an essentially starter if you’re interested in fraud and data science.

I have pretty much read this book cover to cover and it's awful. Terrible typos, but worse than this it doesn't really go into enough detail about the techniques. The examples are also far too simple. I am doing a large fraud project at work and haven't found any of the content remotely useful. The whole section on descriptive statistics is useful, but any analyst would know to do all of this for any preliminary/exploratory work.It needs much more in the latter sections where you actually want to know more about the techniques. I mean there's one lousy paragraph on clustering - how can this be right?! Save your money and get a decent stats book - you'll get much more out of it. Terrible waste of money!

Hace un buen resumen teórico pero no explica el desarrollo de fórmulas ni como se puede aplicar. En mi opinión no cumple con lo que ofrece.

Great book on fraud analytics with enough of depth covering all the key topics enjoyable read

Its an intermediary book for people who have solid data and statistics knowledge. Bought it for my analysts and they found it very useful.