Abstract
The Domingos-Richardson model, along with several other infection models, has a wide range of applications in prediction. In most of these, a fundamental problem arises: the edge infection probabilities are not known. To provide a systematic method for the estimation of these probabilities, the authors have published the Generalized Cascade Model as a general infection framework, and a learning-based method for the solution of the inverse infection problem. In this paper, we will present a case-study of the inverse infection problem. Bankruptcy forecasting, more precisely the prediction of company defaults is an important aspect of banking. We will use our model to predict these bankruptcies that can occur within a three months time frame. The network itself is built from the bank’s existing clientele for credit monitoring issues. We have found that using network models for short term prediction, we get much more accurate results than traditional scorecards can provide. We have also improved existing network models by using inverse infection methods for finding the best edge attribute parameters. This improved model was already implemented in August 2013 to OTP Banks credit monitoring process, and since then it has proven its usefulness.
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Notes
Such attributes are readily available in banking applications.
We will refer to the OTP Bank of Hungary simply as bank from now on.
Uniform 33 % of the cases in each category.
A vertex \(v\) is a supplier if it is at the end of a directed edge.
Here we used a \(N^{++}\) algorithm for community detection, see (Bóta et al. 2010).
The actual outstanding is higher then the given credit-limit.
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Acknowledgments
The first and last authors were partially supported by the European Union and the European Social Fund through project FuturICT.hu (Grant no.: TÁMOP-4.2.2.C-11/1/KONV-2012-0013). The fifth author was partially supported by the European Union and co-funded by the European Social Fund through project HPC (Grant no.: TÁMOP-4.2.2.C-11/1/KONV-2012-0010).
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Bóta, A., Csernenszky, A., Győrffy, L. et al. Applications of the inverse infection problem on bank transaction networks. Cent Eur J Oper Res 23, 345–356 (2015). https://doi.org/10.1007/s10100-014-0375-2
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DOI: https://doi.org/10.1007/s10100-014-0375-2