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

Chapter 1Fraud: Detection, Prevention, and Analytics!

Introduction

In this first chapter, we set the scene for what's ahead by introducing fraud analytics using descriptive, predictive, and social network techniques. We start off by defining and characterizing fraud and discuss different types of fraud. Next, fraud detection and prevention is discussed as a means to address and limit the amount and overall impact of fraud. Big data and analytics provide powerful tools that may improve an organization's fraud detection system. We discuss in detail how and why these tools complement traditional expert-based fraud-detection approaches. Subsequently, the fraud analytics process model is introduced, providing a high-level overview of the steps that are followed in developing and implementing a data-driven fraud-detection system. The chapter concludes by discussing the characteristics and skills of a good fraud data scientist, followed by a scientific perspective on the topic.

Fraud!

Since a thorough discussion or investigation requires clear and precise definitions of the subject of interest, this first section starts by defining fraud and by highlighting a number of essential characteristics. Subsequently, an explanatory conceptual model will be introduced that provides deeper insight in the underlying drivers of fraudsters, the individuals committing fraud. Insight in the field of application—or in other words, expert knowledge—is crucial for analytics to be successfully applied ...