Financial institutions’ trade data management systems are undergoing new changes as a result of unprecedented expansion in market information and corresponding compliance pressure. With these new changes come challenges that bring about the need for effective solutions that enable smooth, ongoing operations and robust risk analytics for trade reconciliation.
Some institutions have tried combating these issues by hiring more people focused solely on compliance and operations, but this isn’t a scalable solution.
This paper will:
- Explore how Tamr enables firms solve transaction data challenges without overhauling their existing infrastructure
- Explain how a human-guided machine learning approach improves data quality and analytics to achieve better decision making and greater capital efficiency