Behavioral analytics for forecasting customer lifetime value, digital banking acceptance, and insurance penetration in underserved U.S. minority communities

Emmanuel Amaara *

Wells Fargo Bank N.A, Pennsylvania, USA.
 
Review
International Journal of Science and Research Archive, 2024, 12(02), 3132-3145.
Article DOI: 10.30574/ijsra.2024.12.2.1490
Publication history: 
Received on 04 July 2024; revised on 23 August 2024; accepted on 28 August 2024
 
Abstract: 
Underserved minority communities across the United States continue to face persistent disparities in financial inclusion, digital banking adoption, and access to insurance products. Traditional market analytics often overlook the behavioral, cultural, and structural drivers shaping financial decision-making in these populations, leading to inaccurate customer lifetime value (CLV) forecasts and limited traction for digital financial services. As financial institutions increasingly rely on predictive analytics to optimize product design and outreach, there is a growing need for frameworks that account for the unique behavioral patterns and contextual constraints faced by marginalized consumer groups. This paper proposes a behavioral-analytics framework that integrates digital engagement metrics, psychographic segmentation, mobile-usage patterns, trust signals, and financial-literacy indicators to improve forecasting accuracy for CLV, digital-banking acceptance, and insurance penetration. The approach evaluates decision heuristics, risk perception, institutional trust, and household financial volatility factors that disproportionately influence financial behavior in minority communities. By combining machine-learning techniques with culturally contextualized behavioral models, the framework identifies latent drivers of adoption, retention, and cross-selling potential that are often invisible in conventional demographic-only segmentation. The paper also examines structural barriers such as digital-access limitations, historical mistrust of financial institutions, and the underrepresentation of minority households in traditional credit datasets. Scenario simulations demonstrate how behavioral variables such as mobile-app interaction frequency, peer influence strength, and savings-habit consistency significantly enhance model performance in predicting long-term value and insurance uptake. The findings highlight the necessity of embedding behavioral insights into financial-product strategies to improve inclusion outcomes. Ultimately, the framework enables financial institutions, fintech innovators, and insurers to design more equitable and data-responsive services that align with the realities of underserved minority communities, thereby strengthening financial resilience and expanding market participation.
 
Keywords: 
Behavioral analytics; Customer lifetime value; Digital banking adoption; Insurance penetration; Minority financial inclusion; Predictive modeling
 
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