Authors:
Sacide Kalayci
and
Secil Arslan
Affiliation:
Yapi Kredi Technology, Turkey
Keyword(s):
Credit Risk, Small and Medium-sized Enterprise, Early NPL Warning, Random Forest.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
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, Supp
ort 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.
(More)