A client had a best in class operational process for its FX business with a 99% STP rate, however, the daily volume of trades meant that a large absolute number of exceptions was generated each day. The client wanted to understand the chain of behaviour that led to exceptions being generated.
- Utilise CPAnalytics ability to connect to several disparate data sources to explore the data, without having to build a data warehouse or data model.
- Layer business rules to connect data across the system to provide a front to back view.
- The flexible, iterative nature of CPAnalytics means that we and clean and build the links as we
explore the data.
- The resulting view allowed us to build the chain of events: how upstream actions led to exceptions
- The solution was entirely data driven and not based on sampling the available data.
Management were provided a view of their business which they had not seen before. CP Analytics’s ability to join up the data to find the patterns allowed the root causes to be revealed. The resulting data discovery model could be implemented in production and run on an automated basis, with alerts based on business rules to detect upstream exception causing events.