Resumen

UMUHOZA, Eric; NTIRUSHWAMABOKO, Dominique; AWUAH, Jane  y  BIRIR, Beatrice. Using Unsupervised Machine Learning Techniques for Behavioral-based Credit Card Users Segmentation in Africa. SAIEE ARJ [online]. 2020, vol.111, n.3, pp.95-101. ISSN 1991-1696.

Given the fierce competition that has come up because of evolving FinTech and e-payment industries in the global market, the credit card industry has become extremely competitive. To survive, financial institutions need to offer their credit card customers with more innovative financial services that provide a personalized customer experience beyond their banking needs. While we are witnessing this high competition that aims to provide better services to credit card holders, Africa risks remaining behind once again: in 2017, the World Bank reported that only 4.47% of Africans aged 15 and above hold a credit card. In this paper, we define and describe the steps that can be taken to build a behavioral-based segmentation model that differentiates African credit cardholders based on their purchases data. We focus on African customers and African financial institutions as (i) little has been done so far when it comes to understanding the spending behavior of African credit card holders; and (ii) because we believe that this segmentation will allow boosting credit card usage in Africa, thus allowing Africans to fully benefit from credit cards as other parts of the world do. The results of this research can help tailor the market campaign to make them customer-centric and reduce the associated marketing costs. We show the proposed approach at work using anonymized credit card data of one the leading banks in Egypt, the Commercial International Bank of Egypt.

Palabras clave : African credit card market; African credit card profiles; Customer segmentation; spending behavior; unsupervised machine learning.

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