Abstract
Financial institutions are exposed to credit risk due to issuance of consumer loans. Thus, developing reliable credit scoring systems is very crucial for them. Since, machine learning techniques have demonstrated their applicability and merit, they have been extensively used in credit scoring literature. Recent studies concentrating on hybrid models through merging various machine learning algorithms have revealed compelling results. There are two types of hybridization methods namely traditional and ensemble methods. This study combines both of them and comes up with a hybrid meta-learner model. The structure of the model is based on the traditional hybrid model of ‘classification + clustering’ in which the stacking ensemble method is employed in the classification part. Moreover, this paper compares several versions of the proposed hybrid model by using various combinations of classification and clustering algorithms. Hence, it helps us to identify which hybrid model can achieve the best performance for credit scoring purposes. Using four real-life credit datasets, the experimental results show that the model of (KNN-NN-SVMPSO)-(DL)-(DBSCAN) delivers the highest prediction accuracy and the lowest error rates.
Highlights
A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ CustomersMachine learning techniques have demonstrated their applicability and merit, they have been extensively used in credit scoring literature
Owing to the recent global financial crisis and European sovereign debt crisis, credit risk assessment has turn out to be an increasingly vital issue for banks and credit institutions throughout the world
As financial institutions are exposed to credit risk when issuing consumer loans, developing reliable credit scoring systems is crucial for them
Summary
Machine learning techniques have demonstrated their applicability and merit, they have been extensively used in credit scoring literature. Recent studies concentrating on hybrid models through merging various machine learning algorithms have revealed compelling results. There are two types of hybridization methods namely traditional and ensemble methods. This study combines both of them and comes up with a hybrid meta-learner model. The structure of the model is based on the traditional hybrid model of ‘classification + clustering’ in which the stacking ensemble method is employed in the classification part. This paper compares several versions of the proposed hybrid model by using various combinations of classification and clustering algorithms. It helps us to identify which hybrid model can achieve the best performance for credit scoring purposes.
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