Fraud Analytics Strategies and Methods for

Fraud Analytics will depict the elements of analysis that are used in today's fraud examinations, fraud investigations, and financial crime investigations. It will review the types of anlysis that should be considered prior to beginning an investigation. For example, in a financial crimes investigation (e.g. money laundering) it helps to create a commodity flow analysis chart associating the "red flags" to suspects and/or organizations. It will show how to use data mining techniques to detect fraud, review the two major data analytics providers, ACL and IDEA and show how valuable a tool this is for investigators and auditors. Throughout, examples will be provided along with sample cases to show readers how the concepts discussed work in practice.

DELENA D. SPANN, MSc, CFE, is employed with the United States Secret Service, Chicago Field Office, where she is assigned to the Electronic and Financial Crimes Task Force.

Spann routinely serves on high-profile financial crimes investigations that include detecting red flags, trends, and anomalies in complex financial transactions. She is frequently called upon as a guest speaker on her expertise in fraud analytics and financial crimes. She is dedicated to the study of white-collar crime.

Spann holds a bachelor's degree in liberal studies from Barry University and a master of science degree in criminal justice administration from Florida International University. She is Board of Regent (Emeritus), an Advisory Board Member, and Higher Education Committee Member of the Association of Certified Fraud Examiners; a Board of Director of ASIS International Economic Crime Council; Education Task Force Member of the Association of Certified Anti-Money Laundering Specialists; Advisory Board Member at Robert Morris University; Executive Director of the Association of Certified Fraud Examiners, Greater Chicago Chapter; a Threat Finance Task Force Member of the Association of Certified Financial Crimes Specialists; and Board of Director (Emeritus), Step Women's Network of Chicago. Spann also serves as an adjunct professor at the university/college level.

Foreword

Preface

Acknowledgments

Chapter 1: The Schematics of Fraud and Fraud Analytics

Report to the Nations on Occupational Fraud and Abuse

Mining the Field: Fraud Analytics in Its New Phase

How Do We Use Fraud Analytics?

Fraud Detection

How Do We Define Fraud Analytics?

Fraud Analytics Refined

Notes

Chapter 2: The Evolution of Fraud Analytics

Why Use Fraud Analytics?

Case Study

The Evolution Continues

Fraud Prevention and Detection in Fraud Analytics

Incentives, Pressures, and Opportunities

Notes

Chapter 3: The Analytical Process and the Fraud Analytical Approach

The Turn of the Analytical Wheel

It Takes More than One Step

Probabilities of Fraud and Where It All Begins

What Should the Fraud Analytics Process Look Like?

Fraud Data Analytics Exposed

Notes

Chapter 4: Using ACL Analytics in the Face of Excel

Case Study: ACL Inventory Fraud of Operation Supply and Demand

The Devil Remains in the Details

Notes

Chapter 5: Fraud Analytics versus Predictive Analytics

Overview of Fraud (Data) Analytics and Predictive Analysis (Modeling)

Comparing and Contrasting Methodologies

13 Step Score Development versus Fraud Analytics

CRISP-DM versus Fraud Data Analysis

SAS / SEMMA versus Fraud Data Analysis

Conflicts within Methodologies

Composite Methodology

Comparing and Contrasting Predictive Modeling and Data Analysis

Notes

Chapter 6: IDEA Data Analysis Software

Detecting Fraud with IDEA

IDEA Case Study

Fraud Analysis Points of IDEA

Correlation, Trend Analysis, and Time Series Analysis

What is IDEA’s Purpose?

A Simple Scheme: The Purchase Fraud of an Employee as a Vendor

Stages of Using IDEA

Notes

Chapter 7: Centrifuge Analytics: Is Big Data Enough?

Sophisticated Link Analysis

Centrifuge Visual Network Analytics for Anti-Counterfeiting

The Challenge with Anti-Counterfeiting

Interactive Analytics: The Centrifuge Way

Case Scenario

Fraud Analysis

The Fraud Management Process

Notes

Chapter 8 i2 Analyst Notebook: The Best in Fraud Solutions

Rapid Investigation of Fraud and Fraudsters

I2 Analyst Notebook

I2 Analyst Notebook and Fraud Analytics

How to Use i2 Analyst’s Notebook: Fraud Financial Analytics

Money Laundering and Using i2 Analyst Notebook Case Scenario and Process

Notes

Chapter 9: The Power to Know Big Data: SAS Visual Analytics and Actionable Intelligence Technology’s-Financial Intelligence System

The SAS Way

AITFIS (Actionable Intelligence Technologies Financial Investigations Systems)

Case Study Using FIS

Notes

Chapter 10: New Trends in Fraud Analytics and Tools

The Many Faces of Fraud Analytics

The Paper Chase Is Over

To Be or Not to Be

Visual Links Analytics

FICO Insurance Manager 3.3

IBM® i2® iBase

Palantir Tech

Fiserv’s AML Manager

Notes

About the Author

Index

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Fraud Analytics Strategies and Methods for Detection and Prevention

Summary

Author Biography

Table of Contents

Supplemental Materials