The contemporary insurance sector, characterized by high attrition and intense competition for specialized talent, necessitates a paradigm shift from reactive recruitment to proactive, data-driven talent acquisition. This research investigates the integration of predictive analytics and digital selection frameworks at HDFC Life Insurance, examining their dual impact on operational recruitment efficiency and internal employer branding. Utilizing a mixed-methods approach comprising quantitative HRIS data analysis and qualitative employee sentiment evaluation, this study develops a machine-learning-informed conceptual model for predicting candidate success and tenure. The findings suggest that algorithmic candidate profiling not only accelerates the time-to-hire by 28% but also significantly aligns candidate values with the organizational culture, acting as a potent tool for internal marketing. Furthermore, the deployment of transparent, AIdriven selection processes enhances perceived organizational justice, thereby strengthening the employer brand from within. This paper provides a comprehensive framework bridging human resources technology and strategic marketing, offering actionable insights for optimizing the employee lifecycle in the financial services industry.

Hina Wasudeo Dhawas, Prof. Ketaki Kukde, "Predictive Analytics in Talent Acquisition: Evaluating Digital Selection Frameworks and Internal Marketing at HDFC Life Insurance" International Journal of Scientific Research and Engineering Development, V9(5): Page(838-842) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.