Development of a business intelligence solution for fraud detection in CEP logistics

The conception of a logical data model and the implementation of the physical model in a cloud DWH based on Snowflake is designed to decouple the business logic for recognizing anomalies in shipment processing as far as possible. This is achieved by only calculating predefined metrics at the relevant process stages. By setting threshold values in the Tableau reports, the user is given the opportunity to individually configure the identification of anomalies. The development of ETL/ELT processes and data pipelines for regular data management is based on Python and SQL scripts. These are orchestrated and scheduled using the Argo Workflow Engine in the form of a directed acyclic graph (DAG). Various Tableau reports serve as an interface to the data with the aim of accessing the detailed data in top-down semantics from aggregated data via drill-down.