Investigation on credit card customer cancellations: A case study of AMEX credit cards

Ruidi Gao

doi:10.54691/bcpbm.v34i.3000

Authors

Ruidi Gao

DOI:

https://doi.org/10.54691/bcpbm.v34i.3000

Keywords:

XGBoost, Credit Card Customer, Machine Learning, Data Mining.

Abstract

American Express (AMEX) is one of the most popular credit card services in the U.S. Lately, the company is facing loss of some valued customers for specific reasons. This paper investigates the customers’ cancellation behavior based on XGBoost in terms of the AMEX data. Besides, the factors contributing to customers’ cancellations are identified, e.g., customer age, total transaction counts, total transaction amount. According to the analysis, the proposed XGBoost model can reach 96.5% in accuracy. By implementation of such tool, it is feasible for the bank to predict their customer behavior and take measures proactively. These results shed light on guiding further exploration of credit card services.

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