This solution also covers event monitoring, with the more diffuse scope of identifying fraud & integrity risk scenario’s or ‘methods of operation’. This requires more complex and a variety of monitoring routines. We have included the work flow and case definitions to provide for specific follow-up of fraud alerts and for fraud investigations, including the commissioning of the relevant risk remediation measures to the appropriate authorities. Robotics and machine learning are more difficult to apply, as fraudster tend to cover their tracks, which leads to corrupted data. Machine learning has a problem in dealing with this kind of data.

Features and processes included are:

  • Combining multiple detection mechanisms and methods of operation,
  • Real-time monitoring, real-time adjustment of rules and scenario’s
  • Audit trail on rules maintenance, including contemplations and journal entries
  • A standard interface for integrating detection signals from 3rd party monitoring applications
  • Aggregation of signals and risks in 1 environment, visualized through link analysis
  • Flexible, data agnostic integration framework for additional source files
  • Strong visualization of complex investigations and information (e.g.: networks, behavior, trends)
  • Standardization in assessment and investigations through the ‘W7 Standard’
  • ‘Privacy by design’ based on the ‘W7 Standard’
  • Standard templates for damage- and cost calculations
  • Standard connectors with IVR/EVR for handling of (case) information and measures

The other ForensicCloud solutions by BusinessForensics

Integrity risk management

Fraud/Integrity surveillance

Proof of value

Are you interested in the HQ products but would you like to experience what our solution can do for your business? Then contact us for more information and a “proof of value”.

During the proof of value we can show what the added value and the features are of the HQ products, what we can do with your data and what your savings will be, in a short timespan and with the use of your own data.