Building a Credit Scoring Model Based on Data Mining Approaches

• Published in: International journal of software engineering and knowledge engineering Vol. 30; no. 2; pp. 147 - 169
• Main Authors: Nalić, Jasmina, Martinovic, Goran
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 01.02.2020; World Scientific Publishing Co. Pte., Ltd
• Subjects: Algorithms; Confidence; Credit scoring; Data mining; Decision making; Decision trees; Generalized linear models; Parameter identification; Pattern recognition; Risk management; Scoring models; Statistical analysis; Statistical models; Support vector machines; generalized linear model (GLM); logistic regression (LR); data mining; Classification; credit scoring
• ISSN: 0218-1940; 1793-6403
• DOI: 10.1142/S0218194020500072

Nowadays, one of the biggest challenges in banking sector, certainly, is assessment of the client’s creditworthiness. In order to improve the decision-making process and risk management, banks resort to using data mining techniques for hidden patterns recognition within a wide data. The main objective of this study is to build a high-performance customized credit scoring model. The model named Reliable client is based on Bank’s real dataset and originally built by applying four different classification algorithms: decision tree (DT), naive Bayes (NB), generalized linear model (GLM) and support vector machine (SVM). Since it showed the greatest results, but also seemed as the most appropriate algorithm, the adopted model is based on GLM algorithm. The results of this model are presented based on many performance measures that showed great predictive confidence and accuracy, but we also demonstrated significant impact of data pre-processing on model performance. Statistical analysis of the model identified the most significant parameters on the model outcome. In the end, created credit scoring model was evaluated using another set of real data of the same Bank.