Introduction
CubeMatch data analysts developed a capability to probe the client’s raw data with the aim of generating a Customer Intelligence report containing high level information and drill-down capability to more granular data. In dealing with data quality issues during the Extract, Transform, and Load (ETL) process, the analysts identified metrics, beyond the original brief, that indicated unusual customer activity. This provided the basis for further investigation and enhanced risk management.
CubeMatch Deliverables
- Report to provide the client with intelligence on customer behaviour
- Identification of key metrics to facilitate interrogation of the data for further clarity
- Pinpoint and document customers that meet the key risk metrics
CubeMatch Approach
- Determine business objectives and detailed requirements
- Ascertain data sources to provide the required data; review data to establish key risk metrics
- Identify sources of data enrichment to augment the reports
- Establish end-to-end flows for data including storage, processing method and final product
- Overcome data quality challenges during the ETL process to provide meaningful data
- Devise manual intervention steps where necessary to overcome lack of automated end-to-end system linkages
- Conduct unit, process, and system testing
- Document approach, environments, language/code used, and transfer knowledge to the business users
Benefits Delivered
- Customer Intelligence and Risk Identification reports
- Additional tools to support the client’s risk management process
- Replicable process allowing for on-going customer risk management
- Basis for additional risk metric identification