Fraud detection can be a hit-or-miss affair in many companies – until the time a hefty sum goes missing or someone is arrested for corruption. In this guide, ACL’s John Verver examines issues around detection of fraud as a standalone function or as part of overall risk management and control.
The techniques range from statistical analysis designed to detect exceptional transactions – those that are outside the norm – through to analytic tests that look for specific circumstances that indicate a high probability of fraud.
One of the most effective techniques is to compare data across different databases and system that are normally not compared, such cross-checking whether a supplier’s bank account (ERP system) is the same as that of an employee (PeopleSoft system). This guide also looks at fraud detection software and practical action steps to take in an implementation.
- Integrating fraud detection
- Role of data analysis
- Capabilities of data analysis
- Software for fraud detection
- Automation of fraud detection analytics and continuous monitoring
- Example fraud tests for key business process areas
- Practical steps for implementation