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Comparative Analysis of Machine Learning Models for Customer Churn Prediction in the U.S. Banking and Financial Services: Economic Impact and Industry-Specific Insights

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Journal of Data Analysis and Information Processing 2024年第3期12卷 388-418页

作者： Omoshola S. Owolabi Prince C. Uche Nathaniel T. Adeniken Oghenekome Efijemue samuel attakorah Oluwabukola G. Emi-Johnson Emmanuel Hinneh Department of Data Science Carolina University Winston-Salem NC USA Department of Computer Science Austin Peay State University Clarkesville TN USA Department of Statistical Sciences Wake Forest University Winston-Salem NC USA

Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of machine learning models for customer churn prediction, focusing on the U.S. context. The research evaluates the performance of logistic regression, random forest, and neural networks using industry-specific datasets, considering the economic impact and practical implications of the findings. The exploratory data analysis reveals unique patterns and trends in the U.S. banking and finance industry, such as the age distribution of customers and the prevalence of dormant accounts. The study incorporates macroeconomic factors to capture the potential influence of external conditions on customer churn behavior. The findings highlight the importance of leveraging advanced machine learning techniques and comprehensive customer data to develop effective churn prevention strategies in the U.S. context. By accurately predicting customer churn, financial institutions can proactively identify at-risk customers, implement targeted retention strategies, and optimize resource allocation. The study discusses the limitations and potential future improvements, serving as a roadmap for researchers and practitioners to further advance the field of customer churn prediction in the evolving landscape of the U.S. banking and finance industry.

关键词： Churn Prediction Machine Learning Economic Impact Industry-Specific Insights Logistic Regression Random Forest Neural Networks

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Blockchain-Based System for Secure and Efficient Cross-Border Remittances: A Potential Alternative to SWIFT

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Journal of Software Engineering and Applications 2024年第8期17卷 664-712页

作者： Omoshola S. Owolabi Emmanuel Hinneh Prince C. Uche Nathaniel T. Adeniken Jennifer A. Ohaegbulem samuel attakorah Oluwabukola G. Emi-Johnson Chinaza S. Belolisa Harold Nwariaku Department of Data Science Carolina University Winston Salem NC USA Patterson School of Business Carolina University Winston Salem NC USA Department of Statistical Sciences Wake Forest University Winston Salem NC USA Department of Computer Science Fisk University Nashville TN USA Harold & Co Procurement and Supply Chain Consulting Lagos Nigeria

This paper proposes a blockchain-based system as a secure, efficient, and cost-effective alternative to SWIFT for cross-border remittances. The current SWIFT system faces challenges, including slow settlement times, high transaction costs, and vulnerability to fraud. Leveraging blockchain technology’s decentralized, transparent, and immutable nature, the proposed system aims to address these limitations. Key features include modular architecture, implementation of microservices, and advanced cryptographic protocols. The system incorporates Proof of Stake consensus with BLS signatures, smart contract execution with dynamic pricing, and a decentralized oracle network for currency conversion. A sophisticated risk-based authentication system utilizes Bayesian networks and machine learning for enhanced security. Mathematical models are presented for critical components, including transaction validation, currency conversion, and regulatory compliance. Simulations demonstrate potential improvements in transaction speed and costs. However, challenges such as regulatory hurdles, user adoption, scalability, and integration with legacy systems must be addressed. The paper provides a comparative analysis between the proposed blockchain system and SWIFT, highlighting advantages in transaction speed, costs, and security. Mitigation strategies are proposed for key challenges. Recommendations are made for further research into scaling solutions, regulatory frameworks, and user-centric designs. The adoption of blockchain-based remittances could significantly impact the financial sector, potentially disrupting traditional models and promoting financial inclusion in underserved markets. However, successful implementation will require collaboration between blockchain innovators, financial institutions, and regulators to create an enabling environment for this transformative system.

关键词： Interoperability Tokens AML/KYC Compliance Peer-to-Peer Transfers Financial Inclusion

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