Early NPL Warning for SME Credit Risk: An Experimental Study

Sacide Kalayci, Secil Arslan

Abstract

In credit risk, besides assessing risk of credit applications, it has been very critical to take a proactive decision by foreseeing the risk of non-performing loan (NPL). In Turkey, recent reports demonstrate that among different credit categories such as consumer, corporate, small and medium-sized enterprises (SME) loans, SMEs reflect the highest NPL ratios. This paper focuses on SME credit behavioural scoring to develop an early NPL warning system after the credit is released. Utilizing application scoring features together with behavioural scoring features, an experimental study of classifying SME customers as non-performing or performing is targeted during lifetime of the credit. The proposed system aims to support a warning 6 months ahead to detect NPL state. Random Forest (RF) algorithm is implemented for NPL state classification of active SME credits. Accuracy results of RF algorithm is compared with different machine learning algorithms like Logistic Regression, Support Vector Machine and Decision Trees. It has been observed that accuracy of RF model is increased when different SME credit product features are added to the model. An accuracy ratio of 82.25% is achieved with RF which over performs all other alternative algorithms.

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Paper Citation


in Harvard Style

Kalayci S. and Arslan S. (2017). Early NPL Warning for SME Credit Risk: An Experimental Study.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-271-4, pages 190-197. DOI: 10.5220/0006496601900197


in Bibtex Style

@conference{kdir17,
author={Sacide Kalayci and Secil Arslan},
title={Early NPL Warning for SME Credit Risk: An Experimental Study},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2017},
pages={190-197},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006496601900197},
isbn={978-989-758-271-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Early NPL Warning for SME Credit Risk: An Experimental Study
SN - 978-989-758-271-4
AU - Kalayci S.
AU - Arslan S.
PY - 2017
SP - 190
EP - 197
DO - 10.5220/0006496601900197