Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection

Preface

It is estimated that a typical organization loses about 5 percent of its revenues due to fraud each year. In this book, we will discuss how state-of-the-art descriptive, predictive and social network analytics can be used to fight fraud by learning fraud patterns from historical data.

The focus of this book is not on the mathematics or theory, but on the practical applications. Formulas and equations will only be included when absolutely needed from a practitioner's perspective. It is also not our aim to provide exhaustive coverage of all analytical techniques previously developed but, rather, give coverage of the ones that really provide added value in a practical fraud detection setting.

Being targeted at the business professional in the first place, the book is written in a condensed, focused way. Prerequisite knowledge consists of some basic exposure to descriptive statistics (e.g., mean, standard deviation, correlation, confidence intervals, hypothesis testing), data handling (using for example, Microsoft Excel, SQL, etc.), and data visualization (e.g., bar plots, pie charts, histograms, scatter plots, etc.). Throughout the discussion, many examples of real-life fraud applications will be included in, for example, insurance fraud, tax evasion fraud, and credit card fraud. The authors will also integrate both their research and consulting experience throughout the various chapters. The book is aimed at (senior) data analysts, (aspiring) data scientists, consultants, ...