Using data mining and analytics to proactively detect fraud is one of the most effective anti-fraud controls because it increases the perception of detection and increases audit effectiveness while decreasing costs and mitigating monetary and emotional losses due to fraud.
Data analytics, including the use of Benford’s Law, can be used to profile entire populations of transactional data to look for anomalies and inconsistencies and/or by analyzing transactions for red flags of fraud concealment.
Common data analytics to identify transactions with higher risk of fraud or error include:
- Identification of employee/vendor relationships
- Indicators of fictitious vendors
- Analysis of purchasing card activity
- Indicators of ghost employees
- Duplicate payments testing
With more than 20 years’ experience using data analytics software, we can develop a data analytics program solution tailored to your organization to help detect and prevent fraudulent transactions, inefficiencies and weaknesses in accounting systems. These specialized services can be provided on a one-time or periodic basis as an outsourced service or consultant to your associates to develop an in-house data analytics program.
