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

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 widespread 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.