Publications

Data Mining for Intelligence, Fraud & Criminal Detection: Advanced Analytics & Information Sharing Technologies
Christopher Westphal
CRC-PRESS

Hardcover: 432 pages
Publisher: CRC; 1 edition (December 16, 2008)
ISBN-10: 1420067230
ISBN-13: 978-1420067231

Post 9/11 has brought about many promises for sharing information, better analysis, and generally making the world a safer place for everyone. Every organization, bureau, agency, and corporation has fundamental analytical needs that traditionally require a significant amount of data integration and resources to best understand the data. Whether trying to identify money laundering, insider trading, insurance fraud, terrorist behavior, or other forms of criminal activity, the analytical processes and system architectures are very similar to each other. In fact, the types of patterns exposed in one domain often can be used in another, and it is often not necessary to reinvest and recreate these capabilities across different industries when a common approach can be used. This book addresses these topics in depth and review the commonality, framework, and infrastructures necessary to implemented and deploy complex analytical systems.

Organized into three main sections, the book fully examines and outlines the future of this field with an insider's perspective and a visionary's insight. This book provides those individuals with an understanding of what approaches are currently available and how they can be most effectively implemented.

Key Features:

• Offers analytical applications for use in detecting money laundering schemes, human smuggling operations, narcotics trafficking, and exposing fraud patterns
• Identifies the principles of integration using multiple sources of data, and compares virtual and traditional information and data warehousing techniques
• Provides numerous examples based on real-world data, systems, and operations
• Provides a significant number of examples based on real-world data, systems, and operations
• Identifies the principles of integration using multiple sources of data, and compares virtual and traditional information and data warehousing techniques
• Offers analytical applications and solutions designed to assist in detecting money laundering schemes, human smuggling operations, narcotics trafficking, and exposing fraud patterns
• Presents analytical approaches heavily based on graph theory (e.g., connect-the-dots), which holds the most promise for understanding large quantities of discrete-valued information