Best Profile Match: An Algorithmic Framework for Optimal Customer Profile Identification in Financial Institutions

Abstract

Financial institutions face critical challenges in managing multiple customer profiles across diverse platforms, resulting in operational inefficiencies and regulatory compliance risks. This article presents the Best Customer Profile Match (BCPM) algorithm, a sophisticated solution for determining the most accurate customer profile in complex banking environments. The multi-level verification architecture employs weighted attribute matching enhanced by SSN-based optimizations to ensure accurate identification while maintaining privacy compliance. The algorithm demonstrates significant improvements in profile accuracy, operational efficiency, and system response times through intelligent data normalization, progressive evaluation techniques, and early match termination capabilities. Implementation benefits include enhanced customer experiences, reduced privacy incidents, decreased administrative costs, improved system synchronization, and optimized performance across digital channels. Future enhancements incorporating machine learning, multi-national identity support, fraud detection integration, and biometric authentication promise to further strengthen this foundational capability for financial institutions operating in increasingly complex technological and regulatory landscapes.