Proceedings of the 8th International Conference on Advanced Research in Management, Business and Finance
Year: 2024
DOI:
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Developing A Model to Predict Defaulting Credit Card Clients Using Business Intelligence Techniques
Mohammad maabdeh, Ruba Obiedat
ABSTRACT:
The number of personal credit defaults has increased rapidly with increased of popularity of giving credit cards from the bank as a service for its customers, also the costumers can get credit cards easily. Because of that, the risk of defaulters has increased, therefor, improving models’ performance to predict defaulting credit card clients is receiving more attention recently. This work examines the significance of attributes to improve results using change on error approach, filtering infogain attributes, Gain ratio, and Gini algorithms. In conclusion, this work proposed a model that provides 12 attributes as the most important subset features to assessing the risk of defaulters. The algorithm that was found to give the best performance with this model is the Multilayer perceptron algorithm.
keywords: Credit card Default, Costumers behavior, Credit Risk assessment, feature selection, Customer Behavior