Abstract
Understanding the behavior of complex financial supply chains is usually difficult due to a lack of data capturing the interactions between financial institutions (FIs) and the roles that they play in financial contracts (FCs). resMBS is an example supply chain corresponding to the US residential mortgage backed securities that were critical in the 2008 US financial crisis. In this paper, we describe the process of creating the resMBS graph dataset from financial prospectus. We use the SystemT rule-based text extraction platform to develop two tools, ORG NER and Dict NER, for named entity recognition of financial institution (FI) names. The resMBS graph comprises a set of FC nodes (each prospectus) and the corresponding FI nodes that are extracted from the prospectus. A Role-FI extractor matches a role keyword such as originator, sponsor or servicer, with FI names. We study the performance of the Role-FI extractor, and ORG NER and Dict NER, in constructing the resMBS dataset. We also present preliminary results of a clustering based analysis to identify financial communities and their evolution in the resMBS financial supply chain.
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