Development of advanced models for predicting banking customer churn and retention

Posted: Sep 23, 2022

• Prof. Liam Rossi
• Prof. Lucas Müller
• Prof. Lucas Petrova

Abstract

The prediction of customer churn represents one of the most critical challenges in the banking industry, with direct implications for revenue stability, customer lifetime value, and competitive positioning. Traditional approaches to churn prediction have predominantly relied on classical machine learning algorithms applied to structured banking data, including transaction frequencies, account balances, and demographic information. While these methods have provided valuable insights, they often fail to capture the complex, multi-dimensional nature of customer decision-making processes, particularly in an era of increasing digital banking interactions and evolving customer expectations. This research introduces a fundamentally novel approach that transcends conventional methodologies by integrating principles from quantum computing, behavioral biometrics, and temporal sentiment analysis. The motivation for this interdisciplinary approach stems from the recognition that customer churn decisions are influenced by a complex interplay of rational economic factors, emotional responses, behavioral patterns, and contextual influences that traditional linear models struggle to capture effectively. By drawing inspiration from quantum probability theory, which naturally accommodates superposition states and probabilistic transitions, our model can represent customers as existing in multiple potential states simultaneously, with their eventual churn or retention decision emerging from complex interactions between various influencing factors. Our research addresses several limitations of existing churn prediction frameworks. First, traditional models typically treat customer behavior as following classical probability distributions, ignoring the quantum-like interference effects that can occur when customers evaluate multiple competing options. Second, most existing approaches rely heavily on transactional data while underutilizing rich behavioral and interactional information available through digital banking platforms. Third, the temporal dynamics of customer sentiment and engagement are often oversimplified or ignored entirely in conventional models. The novelty of our approach lies in its integration of three distinct methodological innovations: a quantum-inspired neural architecture that models customer states as probability amplitudes rather than binary classifications, a multimodal data fusion technique for behavioral biometrics, and temporal sentiment analysis.