Using software to detect improper transactions are techniques that have grown in importance for fraud examiners. This book provides fraud examiners and auditors with step by step guidance on using data extraction and software to detect and prevent fraud. Its implementation guidance is supplemented by more than 60 case studies. Plus, it enables readers to use data to identify known and unknown symptoms of fraud. Auditors and fraud examiners will discover how to leverage data analysis as a powerful tool to detect and prevent fraud from occurring in any organization.
Using software to detect improper transactions are techniques that have grown in importance for fraud examiners. This book provides fraud examiners and auditors with step by step guidance on using data extraction and software to detect and prevent fraud. Its implementation guidance is supplemented by more than 60 case studies. Plus, it enables readers to use data to identify known and unknown symptoms of fraud. Auditors and fraud examiners will discover how to leverage data analysis as a powerful tool to detect and prevent fraud from occurring in any organization.
David Coderre has over twenty years of experience in internal audit, management consulting, policy development, manage-ment information systems, system development, and application implementation areas. He is currently President of CAATS (Computer-Assisted Analysis Techniques and Solutions). He is the author of three highly regarded books on using data analysis for audit and fraud detection.
Inhaltsangabe
Case Studies. Preface. CHAPTER 1: What Is Fraud? CHAPTER 2: Fraud Prevention and Detection. CHAPTER 3: Why Use Data Analysis to Detect Fraud? CHAPTER 4: Solving the Data Problem. CHAPTER 5: Understanding the Data. CHAPTER 6: Overview of the Data. CHAPTER 7: Working with the Data. CHAPTER 8: Analyzing Trends in the Data. CHAPTER 9: Known Symptoms of Fraud. CHAPTER 10: Unknown Symptoms of Fraud. CHAPTER 11: Automating the Detection Process. CHAPTER 12: Verifying the Results. APPENDIX 1: Fraud Investigation Plans. APPENDIX 2: Application of CAATTs by Functional Area. APPENDIX 3: ACL Installation Process. Epilogue. References. Index.
Case Studies. Preface. CHAPTER 1: What Is Fraud? CHAPTER 2: Fraud Prevention and Detection. CHAPTER 3: Why Use Data Analysis to Detect Fraud? CHAPTER 4: Solving the Data Problem. CHAPTER 5: Understanding the Data. CHAPTER 6: Overview of the Data. CHAPTER 7: Working with the Data. CHAPTER 8: Analyzing Trends in the Data. CHAPTER 9: Known Symptoms of Fraud. CHAPTER 10: Unknown Symptoms of Fraud. CHAPTER 11: Automating the Detection Process. CHAPTER 12: Verifying the Results. APPENDIX 1: Fraud Investigation Plans. APPENDIX 2: Application of CAATTs by Functional Area. APPENDIX 3: ACL Installation Process. Epilogue. References. Index.
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