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Dynamic Credit Quality Evaluation with Social Network Data

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  • Stanley Sewe
  • Philip Ngare
  • Patrick Weke

Abstract

We investigate the filtering problem where the borrower’s time varying credit quality process is estimated using continuous time observation process and her (in this paper we refer to the borrower as female and the lender as male) ego‐network data. The hidden credit quality is modeled as a hidden Gaussian mean‐reverting process whilst the social network is modeled as a continuous time latent space network model. At discrete times, the network data provides unbiased estimates of the current credit state of the borrower and her ego‐network. Combining the continuous time observed behavioral data and network information, we provide filter equations for the hidden credit quality and show how the network information reduces information asymmetry between the borrower and the lender. Further, we consider the case when the network information arrival times are random and solve stochastic optimal control problem for a lender having linear quadratic utility function.

Suggested Citation

  • Stanley Sewe & Philip Ngare & Patrick Weke, 2019. "Dynamic Credit Quality Evaluation with Social Network Data," Journal of Applied Mathematics, John Wiley & Sons, vol. 2019(1).
    • Handle: RePEc:wly:jnljam:v:2019:y:2019:i:1:n:8350464
    DOI: 10.1155/2019/8350464
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    References listed on IDEAS

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    1. Yanhao Wei & Pinar Yildirim & Christophe Van den Bulte & Chrysanthos Dellarocas, 2016. "Credit Scoring with Social Network Data," Marketing Science, INFORMS, vol. 35(2), pages 234-258, March.
    2. Leippold, Markus & Vanini, Paolo & Ebnoether, Silvan, 2006. "Optimal credit limit management under different information regimes," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 463-487, February.
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