The client is a top-tier investment bank and faced difficulties in setting up trade surveillance and making it regulatory complaint for MAR and MiFID II 2 due to the absence of quality data engineering skills. They wanted to revamp existing systems to ensure optimal performance for the order of their trade surveillance.
Sigmoid used Spark-based ETL to deliver improvements in the data pipeline in terms of performance and resource optimization, reducing timelines from T+3 to T+1. We also created a platform for ad-hoc surveillance and pipeline to locate wash trades. Some of the other solutions included faster data enrichment, quicker surveillance results, optimizing alert processing workflows, and more.
We successfully resolved the diverse data challenges faced by the bank while creating 4x faster response times and improved performance optimization from T+3 to T+1 timelines. Our ad-hoc surveillance platform delivered significant gains in the overall orchestration.