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Handbook of Human Computation pp 215–245Cite as

Human Computation-Enabled Network Analysis for a Systemic Credit Risk Rating

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Abstract

This chapter proposes a novel approach to credit risk rating based upon Network Analysis and enabled by Human Computation. Credit risk rating, which is essential on financial markets, has become difficult with the advent of financial instruments called derivatives and structured notes and of credit management techniques called securitization. The consequences have been dramatic: A wide-spread improper credit risk rating in the presence of these instruments and techniques has been recognized as a major cause of the financial crisis of 2007–2009 which sparked worldwide recessions. This chapter first proposes to collect risk estimates from debtors and derivatives’ parties and to aggregate these estimates into eigenvector centralities expressing a systemic rating of the credit risk faced by the market’s agents. This rating is shown to hold the promise of overcoming many deficiencies of current credit risk rating. Then, practical and theoretical implications of the proposed approach are discussed. Finally, observing that Human Computation systems and markets are related, it is argued that both Human Computation systems and markets are promising applications for approaches of the kind proposed here.

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Notes

  1. 1.

    This denomination has been suggested by Pietro Michelucci.

  2. 2.

    Surprisingly, self-sufficiency of Human Computation systems does not seem to have, so far, attracted much attention within the research community. The system’s self-sufficiency has been one of the author’s concerns in building the Human Computation platform metropolitalia.org (Kneissl and Bry 2012).

  3. 3.

    Past, present and even hidden information, depending on which of the Weak, Semi-Strong and Strong Efficient Market Hypotheses is considered.

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Acknowledgements

The author is thankful to Norbert Eisinger (from the Institute for Informatics of the Ludwig-Maximilians University of Munich) for useful suggestions; to the team of the Play4Science research project for stimulating discussions on incentives in Human Computation systems; and to Pietro Michelucci, editor-in-chief of the “Handbook of Human Computation”, and Haym Hirsh, editor of Section B “Application Domains” of that handbook, for useful advices on how to better present the research reported about in this chapter.

The work reported about in this chapter has benefited much from the knowledge and the experience the author gained through his contributions to the Human Computation platforms ARTigo.org and metropolitalia.org developed within the project Play4Science founded in part by the German Foundation for Research (DFG, project number 578416).

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  1. Institute for Informatics, Ludwig-Maximilian University of Munich, Oettingenstr. 67, 80538, München, Germany

    François Bry

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  1. ThinkSplash, LLC, Fairfax, Virginia, USA

    Pietro Michelucci

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Bry, F. (2013). Human Computation-Enabled Network Analysis for a Systemic Credit Risk Rating. In: Michelucci, P. (eds) Handbook of Human Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8806-4_19

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