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IMI/Publicaţii/CSJM/Ediţii/CSJM v.33, n.1 (97), 2025/

A Guidance Framework for Resolving Problems of Credit Card Churn Detection Systems

Authors: Fehim Altınışık, Ahmet Sayar
Keywords: Machine learning, artificial intelligence in operation, credit card, churn detection, data synthesis.

Abstract

The study examines possible solutions to resolve problems that occur in the process of operationalizing systems that predict the financial services that customers will terminate using machine learning and business intelligence approaches. The scope of the study consists of defining the problem, collecting and integrating the data, training the models, evaluating and validating the outputs, and making them ready for use in the production environment. In addition, intuition about infrastructure, architecture, processes, technologies, and other artifacts used during the study is included. The data manipulation and pre-processing framework proposed in this study is applicable to both real and synthetic banking data. To implement each step in detail, an improved version of an auxiliary study was used. A study has been carried out in a financial institution in Turkey, chosen as an auxiliary, in which customers who are likely to cancel credit cards are determined by machine learning. The problems, findings, and results are examined in detail. The framework used in this study is believed to be used not only in the integration of credit card product churn detection systems but also in the integration of other systems that use machine learning and deep learning.

Fehim Altınıșık
ORCID: https://orcid.org/0000-0003-1333-6411
Kocaeli University, Department of Computer Engineering.
Kabaoǧlu, Baki Komșuoǧlu Avenue 41001 ˙Izmit Kocaeli/Turkey.
E-mail:

Ahmet Sayar
ORCID: https://orcid.org/0000-0002-6335-459X
Kocaeli University, Department of Computer Engineering.
Kabao˘glu, Baki Komșuoǧlu Avenue 41001 ˙Izmit Kocaeli/Turkey.
E-mail:

DOI

https://doi.org/10.56415/csjm.v33.02

Fulltext

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