Relationship Data: The Missing Link of the Current Financial Infrastructure

Abstract

Improving understanding of the complex relationships among financial entities is critically important for risk managers and for financial authorities charged with multiple policy objectives. High quality information is lacking at both the intra- and inter-enterprise levels and to support analysis of the financial network. Put simply, the current quality and quantity of relationship data is not sufficient to deliver on the ultimate objectives. For the purpose of this discussion we use a very generic definition of a term relationship somewhat similar to the definition used to describe human relationships. And like in the human relationships case we will not claim that only marriage and blood connection makes your relatives, but also loaning a book, loving, hating and sharing a flat. In the financial context, the relationships may be determined by accounting rules set, for example, by IFRS or US GAAP, as well as regulatory requirements in areas of risk management, market integrity, know-your-client, network analysis and statistical consolidation. The financial industry and regulators have spent countless hours arguing and debating the definition of ownership. The problem lies in the question itself. We suggest that as part of any relationship data system the best approach is to put the question aside and avoid a conceptual and practical quagmire. Rather, we recommend collecting and storing less-subjective granular data on theactual legal and economic relationships between firms, which provides a flexibleframework from which any user can answer the question on corporate relationships he or she determines is appropriate at a given time. Encouragingly, technological solutions are available to accommodate this multiplicity of requirements in a single solution. The paper outlines where the practical challenges are that inhibit the development of high quality relationship data and how they can be overcome.

Keywords: relationship data, LEI, UPI, UTI, SDR, TR, swap, OTC derivatives, data standards

JEL Classification: D4, D8, G2, G3