Naslov
Building a Credit Scoring Model Based on Data Mining Approaches

Autori
Nalić, Jasmina ; Martinović, Goran

Izvornik
International journal of software engineering and knowledge engineering (0218-1940) 30 (2020), 2; 147-169

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
classification ; credit scoring ; data mining ; Generalized Linear Model (GLM) ; logistic regression (LR)

Sažetak
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, 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 parameters of great impact (with the greatest) on model outcome. At the end, created Credit scoring model was evaluated using another set of real data of the same bank.

Izvorni jezik
Engleski

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