Credit risk analysis using machine learning algorithms

Sacide Kalaycı · Mustafa E. Kamaşak · Seçil Arslan

14 citations0/0 files storedSignal Processing and Communications Applications Conference (SIU)

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Financial Distress and Bankruptcy Prediction Customer churn and segmentation Imbalanced Data Classification Techniques

Abstract

In credit risk analysis, besides assessing risk of credit applications, taking decision by foreseeing risk of active credit is very important to decrease risk of financial institutions. In Turkey, recent studies reveal that for financial institutions, risk of SME credits is higher than other credit types such as consumer and corporate. Therefore, this paper focuses on predicting SME customer status for period of six months by utilizing application scoring additional to customer behaviour features. By utilizing Random Forest, Neural Networks, Support Vector Machines and Gradient Boosting, performance comparison and also feature analysis for customer behaviour are conducted. Finally, conducted experiments show that utilizing Stacked Generalization methods has positive effect on performance of SME credit risk analysis.

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References

2006Credit scoring with a data mining approach based on support vector machines 2010A comparative assessment of ensemble learning for credit scoring 2009Support vector machines for default prediction of SMEs based on technology credit 1999Not if but When will Borrowers Default 2009A data driven ensemble classifier for credit scoring analysis 2009Least squares support vector machines ensemble models for credit scoring 2001Time will tell: behavioural scoring and the dynamics of consumer credit assessment 2011Knowledge discovery using neural approach for SME’s credit risk analysis problem in Turkey

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