Customer Profitability Optimization Model Using Predictive Analytics in U.S.-Nigerian Financial Ecosystems

Authors

  • Chikaome Chimara Imediegwu  Access Bank PLC, Nigeria
  • Okeoghene Elebe  Access Bank PLC, Nigeria

Keywords:

Customer Profitability Optimization, Predictive Analytics, Financial Ecosystems, Customer Segmentation, FinTech in Nigeria and U.S., Machine Learning in Banking.

Abstract

In the evolving landscape of global finance, institutions increasingly rely on data-driven approaches to maximize customer lifetime value and reduce churn. This review paper explores the design, application, and implications of customer profitability optimization (CPO) models powered by predictive analytics across U.S. and Nigerian financial ecosystems. It highlights how institutions leverage machine learning, behavioral segmentation, and risk-based scoring to forecast revenue streams, manage portfolio costs, and align services with profitable customer segments. Drawing from case studies, regulatory frameworks, and emerging trends in FinTech adoption in both countries, this study critically examines key drivers, such as data availability, digital infrastructure, regulatory harmonization, and cultural context, that influence CPO model effectiveness. The review also identifies barriers to implementation—particularly in Nigerian financial institutions—and offers a comparative analysis of technology deployment strategies in mature (U.S.) versus developing (Nigerian) markets. The paper concludes by proposing a hybrid, scalable CPO framework tailored to bi-regional financial systems, emphasizing cross-border learning and policy innovation to enhance financial performance, inclusion, and resilience.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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2022-10-30

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Volume 8, Issue 5, September-October-2022

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Chikaome Chimara Imediegwu, Okeoghene Elebe, " Customer Profitability Optimization Model Using Predictive Analytics in U.S.-Nigerian Financial Ecosystems" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 5, pp.476-497, September-October-2022.