Data Integration for Systemic Risk in the Financial System
Arnon S. Rosenthal, Len Seligman · Cambridge University Press eBooks · 2013
The need to integrate data from many sources is likely to be a key bottleneck in the analysis of systemic risk. Oversimplified approaches result in large amounts of code being written and maintained by hand, and many software engineers having access to sensitive data. We describe the separate dimensions of the problem – many “silver bullets” only address one or two. We explain why it is neither feasible nor desirable to use a single standard for all data sharing for financial research, and then explore the pros and cons of standards at different levels of detail. We then explore some of the tools needed to implement and evolve a set of data exchanges. Finally, we discuss how data difficulties raise important research issues for both financial analysis experts and computer scientists. The systemic risk data integration challenge Systemic risk analysis in the financial sector requires massive data integration. Information must be collected from hundreds (and ultimately, perhaps, thousands) of financial firms, corresponding entities must be matched, and all the data must be transformed to meet the needs of systemic risk analysis models. The Dodd–Frank act established an Office of Financial Research (OFR) and gave it the mission of collecting, integrating, and analyzing diverse data in order to better track and analyze financial systemic risk. The data integration challenge is daunting and, if handled poorly, will be a significant impediment to the OFR being able to do systemic analyses.