Solvency prediction for small and medium enterprises in banking

Citation data:

Decision Support Systems, ISSN: 0167-9236, Vol: 102, Page: 91-97

Publication Year:
2017
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Citations 1
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DOI:
10.1016/j.dss.2017.08.001
Author(s):
Silvia Figini; Federico Bonelli; Emanuele Giovannini
Publisher(s):
Elsevier BV
Tags:
Business, Management and Accounting; Computer Science; Psychology; Arts and Humanities; Decision Sciences
article description
This paper describes novel approaches to predict default for SMEs. Multivariate outlier detection techniques based on Local Outlier Factor are proposed to improve the out of sample performance of parametric and non-parametric models for credit risk estimation. The models are tested on a real data set provided by UniCredit Bank. The results at hand confirm that our proposal improves the results in terms of predictive capability and support financial institutions to make decision. Single and ensemble models are compared and in particular, inside parametric models, the generalized extreme value regression model is proposed as a suitable competitor of the logistic regression.