IMPACT OF SBI RETIAL BANKING ON CUSTOMER SATISFACTION

In the modern digital era, customer satisfaction has become a critical metric for evaluating the success and competitiveness of retail banking services. This study explores the impact of State Bank of India (SBI) retail banking offerings on customer satisfaction using advanced analytical techniques grounded in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). The objective is to move beyond traditional surveys and statistical tools by leveraging data-driven models that can predict satisfaction levels, segment customers, and extract meaningful insights from feedback.The research begins with the collection of structured and unstructured data, including service usage metrics, transaction histories, and customer reviews from digital platforms. ML models such as Decision Trees, Support Vector Machines (SVM), and K-Means clustering are employed to classify and segment customers based on their interactions, preferences, and satisfaction scores. Furthermore, DL models—particularly Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks—are used to forecast customer satisfaction trends and understand behavioral patterns over time.To deepen the analysis, Natural Language Processing (NLP) techniques are applied to customer feedback and reviews to perform sentiment analysis. This helps in identifying the key factors that influence satisfaction, such as ease of digital banking, responsiveness of customer service, ATM accessibility, and loan processing time. The findings suggest that AI-driven models provide significantly more accurate and actionable insights than conventional methods, empowering SBI to enhance service quality and customer retention. The study concludes by recommending the integration of predictive analytics and sentiment monitoring into SBI’s customer relationship management systems for realtime experience optimization.