Novel approaches to banking sector customer feedback systems and service improvement

Posted: Jun 02, 2020

• Dr. Scarlett Müller
• Dr. Sophia Park
• Dr. Victoria Mendes

Abstract

Traditional banking sector customer feedback systems have long relied on standardized surveys, complaint forms, and periodic satisfaction metrics that fail to capture the nuanced, real-time customer experience. This research introduces a paradigm shift through the development and validation of a multimodal affective computing framework that integrates biometric response analysis, natural language processing of unstructured feedback, and cross-domain knowledge transfer from clinical AI systems. Drawing inspiration from recent advances in clinical data science, particularly the transfer learning methodologies pioneered by Khan et al. (2019) for autism data scarcity, we adapt and refine these approaches to overcome the limitations of sparse banking feedback data. Our system employs facial expression analysis, galvanic skin response monitoring during banking interactions, and sentiment-aware text mining to create a comprehensive customer experience profile. The methodology demonstrates how techniques developed for healthcare applications can be successfully translated to financial services, creating a novel feedback ecosystem that captures both explicit and implicit customer responses. Experimental results from a six-month deployment across three major banking institutions reveal a 47