Real-Time Augmented Analytics For Enhancing Customer Experience In BFSI

Abstract

Due to the swift digitization across India’s dynamic financial ecosystem, there is a soaring need for innovative solutions that uphold quality in the delivery of customer experience while also upholding stringent operations and compliance. In this paper, a new end-to-end model, REACT Framework (Realtime Enhanced Analytics for Customer Trust), is introduced to preserve customer privacy and generate actionable insights from the augmented analytics, artificial intelligence, machine learning, and federated learning, all with a new comprehensive study. Using a strong dataset of 1.2 million customer interactions over a number of BFSI channels, the research exploits a thoroughly blended technique, alongside state-of-the-art empirical statistics and intensive qualitative interviews of BFSI market players. Real analytics implementation enables boosting customer satisfaction scores by 27\% and response time by 42\%. REACT Framework allows proactive engagement, instant query resolution, quicker decision-making, and better fraud and compliance monitoring. Further, it is intended to facilitate integration with newer technologies such as generative AI and blockchain and allow for the growth of the platform for cross-border BFSI operations. The research also deals with critical challenges such as privacy of data, security, and organizational change management.