Detect fraud earlier to mitigate loss and prevent cascading damage
Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques 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 more, giving you a highly practical framework for fraud prevention.
It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak.
* Examine fraud patterns in historical data
* Utilize labeled, unlabeled, and networked data
* Detect fraud before the damage cascades
* Reduce losses, increase recovery, and tighten security
The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.
Table des matières
Chapter 1: Fraud: Detection, Prevention & Analytics!
Introduction
Fraud!
Fraud Detection and Prevention
Big Data for Fraud Detection
Data Driven Fraud Detection
Fraud Detection Techniques
Fraud Cycle
The Fraud Analytics Process Model
Fraud Data Scientists
A Scientific Perspective on Fraud
References
Chapter 2: Data Collection, Sampling and Preprocessing
Introduction
Types of Data Sources
Merging Data Sources
Sampling
Types of Data Elements
Visual Data Exploration and Exploratory Statistical Analysis
Benford’s Law
Descriptive Statistics
Missing Values
Outlier Detection and Treatment
Red Flags
Standardizing Data
Categorization
Weights Of Evidence Coding
Variable Selection
Principal Components Analysis
Ridits
PRIDIT Analysis
Segmentation
References
Chapter 3: Descriptive Analytics for Fraud Detection
Introduction
Graphical Outlier Detection Procedures
Statistical Outlier Detection Procedures
Clustering
One Class SVMs
References
Chapter 4: Predictive Analytics for Fraud Detection
Introduction
Target Definition
Linear Regression
Logistic Regression
Variable Selection for Linear and Logistic Regression
Decision Trees
Neural Networks
Support Vector Machines
Ensemble Methods
Multiclass Classification Techniques
Evaluating Predictive Models
Other Performance Measures for Predictive Analytical Models
Developing Predictive Models for Skewed Data Sets
Fraud Performance Benchmarks
References
Chapter 5: Social Network Analysis for Fraud Detection
Networks: Form, Components, Characteristics and their Applications
Is Fraud a Social Phenomenon? An Introduction to Homophily
Impact of the Neighborhood: Metrics
Community Mining: Finding Groups of Fraudsters
Extending the Graph: Towards a Bipartite Representation
Case Study: GOTCHA!
References
Chapter 6: Fraud Analytics: Post Processing
Introduction
The Analytical Fraud Model Lifecycle
Model Representation
Selecting the Sample to Investigate
Fraud Alert and Case Management
Visual Analytics
Backtesting Analytical Fraud Models
Model Design and Documentation
References
Chapter 7: Fraud Analytics: A Broader Perspective
Introduction
Data Quality
Privacy
Capital Calculation for Fraud Loss
An Economic Perspective on Fraud Analytics
In- Versus Outsourcing
Modeling Extensions
The Internet of Things
Corporate Fraud Governance
A propos de l’auteur
BART BAESENS is a full professor at KU Leuven, and a lecturer at the University of Southampton. He has done extensive research on analytics, customer relationship management, web analytics, fraud detection, and credit risk management. He regularly advises and provides consulting support to international firms with respect to their analytics and credit risk management strategy.
VÉRONIQUE VAN VLASSELAER is a Ph D researcher in the Department of Decision Sciences and Information Management at KU Leuven. Her research focuses on the development of new techniques for fraud detection by combining predictive and network analytics.
WOUTER VERBEKE is an assistant professor at Vrije Universiteit Brussel (Brussels, Belgium). His research is situated in the field of predictive analytics and complex network analysis with applications in fraud, marketing, credit risk, human resources management, and mobility.
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