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Introduction

In recent years, the credit card industry has experienced significant growth, with more consumers relying on credit cards for their daily transactions. With this increase in credit card usage, the issue of customer churn has become a major concern for credit card companies. Customer churn, or customer attrition, refers to the rate at which customers stop using a company’s products or services. Identifying and predicting customer churn is crucial for credit card companies as it allows them to proactively address customer dissatisfaction and implement retention strategies to prevent customers from leaving.

This thesis focuses on customer churn prediction in the credit card industry using transaction data and machine learning techniques. By analyzing transaction data, credit card companies can gain insights into customer behavior and identify patterns that indicate potential churn. Machine learning algorithms can then be used to build predictive models that forecast which customers are at risk of churning, allowing companies to take proactive measures to retain these customers.

Through this research, we aim to address the following objectives:
1. To investigate the factors influencing customer churn in the credit card industry.
2. To develop a machine learning model for predicting customer churn based on transaction data.
3. To evaluate the performance of the proposed model in predicting customer churn.

This study is not without its limitations, including the availability and quality of data, as well as the complexity of customer behavior. However, by clearly defining the scope of the study and outlining the significance of the research, we hope to provide valuable insights for credit card companies seeking to improve customer retention strategies.

Table of Contents

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Customer Churn in the Credit Card Industry
2.2 Factors Influencing Customer Churn
2.3 Transaction Data Analysis
2.4 Machine Learning for Customer Churn Prediction
2.5 Previous Studies on Customer Churn Prediction
2.6 Comparison of Machine Learning Algorithms
2.7 Evaluation Metrics for Predictive Models
2.8 Data Preprocessing Techniques
2.9 Feature Engineering
2.10 Model Interpretability

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Model Development
3.6 Model Evaluation
3.7 Cross-Validation
3.8 Hyperparameter Tuning

Chapter 4: Discussion of Findings
4.1 Descriptive Statistics
4.2 Feature Importance Analysis
4.3 Model Performance Evaluation
4.4 Interpretation of Results
4.5 Comparison with Previous Studies
4.6 Implications for Credit Card Companies
4.7 Limitations of the Study
4.8 Recommendations for Future Research

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Recommendations for Credit Card Companies
5.5 Conclusion

Thesis Overview

Customer churn prediction in the credit card industry is a critical issue that requires effective strategies to retain customers and improve overall customer satisfaction. This thesis aims to investigate customer churn in the credit card industry using transaction data and machine learning techniques. By analyzing transaction data and building predictive models, credit card companies can identify customers at risk of churning and implement targeted retention strategies.

The literature review will provide a comprehensive overview of previous studies on customer churn prediction, factors influencing churn, transaction data analysis, and machine learning techniques. The research methodology section will outline the research design, data collection, data preprocessing, model development, and evaluation techniques used in this study.

The discussion of findings will present the descriptive statistics, feature importance analysis, model performance evaluation, and implications for credit card companies. The conclusion and summary section will summarize the findings, highlight contributions to the field, provide practical implications, and offer recommendations for credit card companies.

Overall, this thesis aims to provide valuable insights into customer churn prediction in the credit card industry, with the ultimate goal of helping credit card companies improve customer retention strategies and enhance customer satisfaction.

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