Greatly reduce fraud risk by leveraging artificial intelligence and continually monitoring policy, process, and control compliance.
Automated Corporate Fraud Prevention
Proactive Fraud Detection
The rise in system digitisation has increased the risks associated with fraud and spend abuse risks. These risks, if not managed effectively can cause great financial or reputational loss to your organisation.
With the ever-increasing volume of data and the sophistication of fraudsters, organisations can no longer solely rely on system controls or manual checks to detect and prevent fraudulent activity.
Prevention is better than Recovery
Leverage AI and machine learning techniques to analyse data across multiple systems and identify patterns of spend abuse or fraudulent behaviour sooner greatly reducing financial loss and reputational risk.
Ensure the key controls implemented to prevent spend abuse or fraud are operating effectively as intended on a continuous basis.
The Satori Solution
Satori is a Software as a Service that offers automated, AI-driven, continuous control monitoring to detect and prevent fraud across any process within your organisation.
The Solution’s workflow manages exception output to ensure follow-up in a timely manner by the team or the person of responsibility. Each exception is investigated and closed out with a defined issue, action, and root cause.
Satori’s Dashboards provide management with real-time visibility on what issues are occurring and their root causes, enabling opportunities for process improvements.
Satori for Corporate Fraud Prevention
Prevent Business Fraud
- Unverified bank account details
- Vendor details matching employee details
- Same requester and approver
- Suspicious vendors
- Potential ghost employees
- Timesheet abuse
- Unauthorised personal expenditure of corporate cards
”Millions saved from one test of procurement data that detected a years-long fraud, fuelled by a gambling debt. The case went to Court and there was a conviction.
Large ASX Listed BusinessChief Financial Officer
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