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Using Conceptual Models to Theorize about the Relationship Between Records and Risk in the Global Financial Crisis

  • Kafui MonuEmail author
  • Victoria Lemieux
  • Lior Limonad
  • Carson Woo
Chapter
  • 3.4k Downloads

Abstract

Financial records can be used to help identify operational and systemic risks associated with financial transactions since they provide evidence of how a transaction may occur. They are necessary for regulation of the financial system and in order to assert legal and financial claims. In some cases financial institutions do not adequately record information, which can lead to many problems, as was the case during and in the aftermath of the financial crisis of 2007–2009. In these cases it would be useful to understand exactly how missing or incomplete records affected the situation, why they were missing or incomplete, in what ways this situation may have contributed to risk buildup in the financial system, and how the situation could be rectified or even prevented. In this study we suggest using conceptual modelling, a technique more often used for systems development, to gain an understanding of financial records creation, transmission, and management in the processes along the private label residential mortgage-backed securities originate to distribute supply chain. Conceptual modelling is the act of representing the physical and social domain using specific abstractions of it. The study discusses how three different conceptual modelling techniques were used to explore the relationship between records and risk in the context of the financial crisis. From experiences with using these different conceptual modelling approaches, we conclude that conceptual modelling is valuable as a tool to help understand and model relationships and dynamics in financial risk management and as a tool to generate new insights that would otherwise have been more difficult to see.

Keywords

Financial Crisis Financial System Financial Transaction Information Problem Private Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kafui Monu
    • 1
    • 2
    Email author
  • Victoria Lemieux
    • 2
    • 3
  • Lior Limonad
    • 1
    • 2
    • 4
  • Carson Woo
    • 1
  1. 1.Sauder School of Business, University of British ColumbiaVancouverCanada
  2. 2.Centre for the Investigation of Financial Electronic RecordsVancouverCanada
  3. 3.School of Library, Archival and Information Studies, University of British ColumbiaVancouverCanada
  4. 4.IBM Haifa Research LabHaifaIsrael

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