Work

Developing a model to predict defaulting credit card clients using business intelligence techniques

Year: 2025

Type: conference-paper

Abstract: The number of personal credit defaults has increased rapidly with the increased popularity of issuing credit cards by banks as a service to customers. Therefore, the risk of defaulters has increased, hence, improving models’ performance to predict defaulting credit card clients is receiving more attention recently. This work aims to develop a model for predicting credit card defaulters by examines the significance of attributes. The results improved by using features selection techniques including 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 Multilayer perceptron algorithm found to give the best performance among other algorithms tested.

Source: Proceedings of The International Conference on Advanced Research in Management Business and Finance

DOI: 10.33422/icmbf.v1i1.897

Authors: Mohammad Maabdeh, Ruba Obiedat

Institution: University of Jordan

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W4412371378

FWCI:

Cites: 14

Cited by:

Related to: 10