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Neural Computing and Applications
Published in: Neural Computing and Applications 1/2013

01-12-2013 | Original Article

Assessment of the effect on technical efficiency of bad loans in banking industry: a principal component analysis and neuro-fuzzy system

Authors: Firoozaeh Hajialiakbari, Mohamad H. Gholami, Jinus Roshandel, Loghman Hatami-Shirkouhi

Published in: Neural Computing and Applications | Special Issue 1/2013

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Abstract

This paper presents the integration of principal component analysis (PCA) and adaptive network-based fuzzy inference system (ANFIS) to assess the impact of bad loans on technical efficiency of banks. Bad loans or non-performing loans (NPLs) include past due loans, bankrupt and quasi-bankrupt assets and doubtful assets. Bad loans are considered as a bad output for calculation of the technical efficiency through PCA. ANFIS is used to model the relationship between bad loans and technical efficiency. ANFIS modeling is used to capture the nonlinearity and fuzziness existed in the modeling environment. In the ANFIS model, technical efficiency is considered as the output which is modeled with respect to bad loans, profit and costs. The results of the proposed model are illustrated through a case study in Iranian governmental banks. It is evidenced that the effects of bad loans on technical efficiency of banks are not linear but a nonlinear negative impact.
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Metadata
Title
Assessment of the effect on technical efficiency of bad loans in banking industry: a principal component analysis and neuro-fuzzy system
Authors
Firoozaeh Hajialiakbari
Mohamad H. Gholami
Jinus Roshandel
Loghman Hatami-Shirkouhi
Publication date
01-12-2013
Publisher
Springer London
Published in
Neural Computing and Applications / Issue Special Issue 1/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-013-1413-z

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