In January of this year, we published research based on a survey with leading financial institutions which revealed that 65% of financial institutions are struggling to complete the FATCA client identification process efficiently due to gapped, siloed, semi-structured and unstructured data.
Since the publication of this research, we have engaged extensively with financial institutions from all across Europe and North America who claim that that data management is not only confounding their ability to comply with FATCA, but with a whole host of anticipated new regulations (Dodd-Frank, EMIR, and MiFID II being the main ones) too.
While there is a deep understanding of the industry that client and counterparty data management, in particular, will become a vital piece for complying with each of these new regulations (in addition to existing regulations), there lacks an ability to execute policies, procedures, and solutions for a well-designed client and data management system.
This paper takes an in-depth look at how proactive management of client and counterparty data can enable key decision makers to make trusted decisions based on cleansed and standardized data. We advocate taking an integrated, holistic approach to client and counterparty data management to enable the accurate measurement of exposure, risk, and profitability. As part of this, we address the data structures required to support a robust CCDM system, as well as some of the simple planning steps that will assist in a smooth implementation process. We will discuss the mechanisms for the initial population of the CCDM system from disparate sources and, finally, outline the options for the ongoing data interaction between systems.