Digital banking behaviour, creditworthiness prediction, and personalized financial planning for unserved U.S. immigrant populations using analytics-based decision systems
Customer Insights and Business Reports, Compass Group, New Jersey, USA.
Review Article
World Journal of Advanced Research and Reviews, 2023, 20(01), 1410-1422
Publication history:
Received on 29 August 2023; revised on 24 October 2023; accepted on 28 October 2023
Abstract:
Large segments of the U.S. immigrant population remain unserved or underserved by mainstream financial institutions, limiting their access to credit, savings tools, and long-term financial planning resources. These gaps persist due to structural barriers such as thin or non-existent credit files, nontraditional income patterns, cultural differences in banking behavior, and limited trust in formal institutions. As digital financial ecosystems expand, analytics-driven decision systems offer a powerful avenue for understanding and supporting the unique financial journeys of immigrant households. This paper presents an integrated analytical framework designed to model digital banking behavior, predict creditworthiness using alternative data, and deliver personalized financial planning recommendations for unserved immigrant populations. The framework leverages transaction metadata, remittance behavior, mobile-app interaction patterns, cash-flow stability signals, community network influences, and financial-literacy indicators. Machine learning and behavioral analytics are applied to derive risk profiles that go beyond conventional credit scoring, capturing informal financial habits and culturally specific banking preferences. The study demonstrates how digital behavioral patterns such as payment timing, bill-pay consistency, savings frequency, and digital engagement depth can reliably predict creditworthiness in populations excluded from traditional scoring systems. The framework further integrates rule-based and AI-driven advisory engines to deliver tailored financial-planning pathways that support savings goals, debt management, and long-term asset growth. Results show that analytics-based decision systems significantly enhance risk assessment accuracy, reduce approval bias, and improve personalization of financial products for immigrant communities. By combining behavioral modeling, alternative-data scoring, and individualized planning tools, the framework supports a more inclusive financial ecosystem capable of expanding economic mobility and equitable access to financial services.
Keywords:
Digital Banking Behavior; Alternative Credit Scoring; Immigrant Financial Inclusion; Behavioral Analytics; Personalized Financial Planning; Predictive Risk Modeling
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0