AI-DRIVEN WORKFORCE OPTIMIZATION IN E-BANKING: CHALLENGES AND OPPORTUNITIES IN TAMIL NADU’S PUBLIC SECTOR BANKS

Abstract

E-banking, and indeed the general digital revolution in the banking industry, has brought sweeping changes in the way operations are carried out and services are delivered to customers by the public sector banks in India. Although these innovations have enhanced service delivery and operational performance, they have also created urgent issues to do with workforce flexibility, digital skills gaps, and refusal to embrace technology. Current methods tend to separate employee performance measurements and digital transition plans, which create discontinuity in workforce development efforts. To overcome these issues, this paper suggests an AI-based framework that would help to improve the efficiency of the workforce about e-banking implementation in the public sector banks of Tamil Nadu. Data Survey-based data, usage logs, and performance indicators are used to synthesize a comprehensive picture of employee preparedness in the proposed methodology. The prediction of performance, upskilling needs, and adaptive training paths are carried out using advanced machine learning models (e.g., Random Forest, XGBoost), clustering (K-Means, DBSCAN), and personalized recommendation systems. Also, sentiment analysis and graph-based modelling are useful in the provision of strategic feedback and succession planning. The study provides an original conceptual framework, dubbed the 3E-AI Model (Enable, Enhance, Evaluate), which formally organizes the capability of the workforce, provides training personalization, and institutionalizes the AI in the decision-making process. The results provide practical recommendations that banking leaders may consider aligning human capital development with the current digital transformation initiatives to eventually create a sustainable banking workforce that is ready to face the future.