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TitleFraud data analytics methodology : the fraud scenario approach to uncovering fraud in core business systems
Author Leonard W. Vona
ImprintHoboken, New Jersey : John Wiley & Sons, Inc., [2017]
Descript xi, 388 pages ; 24 cm


Fraud Data Analytics Methodology addresses the need for clear, reliable fraud detection with a solid framework for a robust data analytic plan. By combining fraud risk assessment and fraud data analytics, you'll be able to better identify and respond to the risk of fraud in your audits. Proven techniques help you identify signs of fraud hidden deep within company databases, and strategic guidance demonstrates how to build data interrogation search routines into your fraud risk assessment to locate red flags and fraudulent transactions. These methodologies require no advanced software skills, and are easily implemented and integrated into any existing audit program. Professional standards now require all audits to include data analytics, and this informative guide shows you how to leverage this critical tool for recognizing fraud in today's core business systems. Fraud cannot be detected through audit unless the sample contains a fraudulent transaction. This book explores methodologies that allow you to locate transactions that should undergo audit testing.-- Provided by Publisher


Chapter 1: Introduction to Fraud Data Analytics -- Chapter 2: Fraud Scenario Identification -- Chapter 3: Data Analytics Strategies for Fraud Detection -- Chapter 4: How to Build a Fraud Data Analytics Plan -- Chapter 5: Data Analytics in the Fraud Audit -- Chapter 6: Fraud Data Analytics for Shell Companies -- Chapter 7: Fraud Data Analytics for Fraudulent Disbursements -- Chapter 8: Fraud Data Analytics for Payroll Fraud -- Chapter 9: Fraud Data Analytics for Company Credit Cards -- Chapter 10: Fraud Data Analytics for Theft of Revenue and Cash Receipts -- Chapter 11: Fraud Data Analytics for Corruption Occurring in the Procurement Process -- Chapter 12: Corruption Committed by the Company -- Chapter 13: Fraud Data Analytics for Financial Statements -- Chapter 14: Fraud Data Analytics for Revenue and Accounts Receivable Misstatement -- Chapter 15: Fraud Data Analytics for Journal Entries -- Appendix A: Data Mining Audit Program for Shell Companies

Auditing Forensic accounting Fraud -- Prevention Auditing Internal

Chula Business School Library658.473 V945F 2017DUE 30-12-20


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