Smart Banking Using AI-STF Model for a Greener Future: Transforming Sustainable Customer Targeting in India’s Top Private Banks

Abstract

In the era of rapid technological advancement, top private commercial banks in India are increasingly turning to artificial intelligence (AI) to align their marketing and operational strategies with sustainable goals (SGs). This study explores how AI-driven data analytics can support banks in identifying and targeting customer segments that value sustainable banking practices. By analyzing consumer behavior, transaction histories, and socio-economic data, AI models enable banks to craft personalized and sustainable banking solutions while optimizing resource use and minimizing environmental impact. This study adopts a descriptive and conceptual approach, utilizing secondary data, and proposes a new AI-Sustain Targeting Framework (AI-STF) Model.This study emphasizes the need for customer segmentation that promotes sustainable banking products, such as green loans, eco-friendly investment options, and digital banking channels. This study provides insights into the advantages of AI for sustainable practices, potential challenges in implementation, and ethical considerations surrounding data privacy and security in AI-driven customer targeting processes.The study highlights that AI can significantly enhance customer segmentation by analyzing diverse data sources, enabling the design of personalized sustainable banking solutions, such as green loans and eco-friendly investments. The proposed AI-Sustain Targeting Framework (AI-STF) demonstrates the potential to bridge operational efficiency with sustainability goals using real-time feedback and advanced analytics to dynamically refine offerings. Furthermore, adopting sustainability scoring systems ensures quantifiable measures of ecological and economic impact, aligning banking practices with environmental and societal objectives, while promoting customer loyalty and satisfaction.