A neural network approach for credit risk evaluation

Eliana Angelini, Giacomo di Tollo and Andrea Roli

The Quarterly Review of Economics and Finance, 2008, vol. 48, issue 4, 733-755

Abstract: The Basel Committee on Banking Supervision proposes a capital adequacy framework that allows banks to calculate capital requirement for their banking books using internal assessments of key risk drivers. Hence the need for systems to assess credit risk. Among the new methods, artificial neural networks have shown promising results. In this work, we describe the case of a successful application of neural networks to credit risk assessment. We developed two neural network systems, one with a standard feedforward network, while the other with a special purpose architecture. The application is tested on real-world data, related to Italian small businesses. We show that neural networks can be very successful in learning and estimating the in bonis/default tendency of a borrower, provided that careful data analysis, data pre-processing and training are performed.

Keywords: Credit; risk; Basel; II; Neural; networks (search for similar items in EconPapers)
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (41)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1062-9769(07)00076-2
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:quaeco:v:48:y:2008:i:4:p:733-755

Access Statistics for this article

The Quarterly Review of Economics and Finance is currently edited by R. J. Arnould and J. E. Finnerty

More articles in The Quarterly Review of Economics and Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().