Systematic evaluation of banking customer behavior patterns using advanced analytics techniques

Posted: Oct 28, 2025

• Dr. Olivia Schmidt
• Dr. Prof. Abigail Kumar
• Dr. Prof. Anna Moretti

Abstract

This research presents a comprehensive systematic evaluation of banking customer behavior patterns through the application of advanced analytics techniques. Traditional approaches in banking analytics have primarily focused on transaction frequency and credit scoring, leaving significant gaps in understanding the nuanced behavioral patterns that drive customer decisions. Our study introduces a novel multi-modal analytical framework that integrates temporal sequence analysis, sentiment-driven transaction categorization, and cross-channel behavioral mapping to uncover previously unrecognized customer segments and behavioral pathways. We developed a proprietary algorithm that processes heterogeneous banking data streams including transaction histories, digital banking interactions, customer service engagements, and macroeconomic indicators. The methodology employs an innovative fusion of graph neural networks with temporal convolutional networks to model complex customer relationships and behavior evolution over time. Our analysis of a comprehensive dataset spanning three years and over 2.5 million customer interactions revealed seven distinct behavioral archetypes that transcend traditional demographic or wealth-based segmentation. Particularly noteworthy is our discovery of the 'Strategic Saver' archetype, characterized by sophisticated financial maneuvering across multiple account types in response to microeconomic signals, and the 'Digital Native Conservative' segment that exhibits high digital engagement while maintaining traditional conservative financial behaviors. The findings demonstrate that customer financial behaviors follow predictable temporal patterns influenced by both personal life events and broader economic conditions. This research contributes a new paradigm for understanding banking customer behavior that moves beyond static segmentation to dynamic, multi-dimensional profiling, with significant implications for personalized banking services, risk management, and customer retention strategies.