• Rajavel.R
• Dr. Guru K

This study examines the effectiveness of a personalized reinforcement learning (RL)–based training framework in enhancing the competency development of life insurance advisors. A controlled experimental design was implemented involving seventy-five life insurance advisors from Villupuram District, Tamil Nadu, who were divided into two groups: one receiving conventional training and the other undergoing RL-driven adaptive training. Competency levels were assessed before and after the intervention using a comprehensive multi-dimensional evaluation rubric encompassing knowledge, decision-making ability, customer engagement, and performance consistency. The results reveal a statistically significant improvement in the competencies of advisors trained through the RL-based model compared to those trained using traditional methods. The findings demonstrate that reinforcement learning enables dynamic personalization of training interventions, accelerates skill acquisition, and reduces performance variability among advisors. This study highlights the potential of RL-driven training systems as an effective strategy for improving workforce capability and decision-making efficiency in the life insurance sector.

Life Insurance Advisors; Competency Enhancement; Reinforcement Learning; Adaptive Training Systems; Personalized Learning; Decision-Making Skills; Performance Optimization.

"Advancing Competency Development among Life Insurance Advisors in Villupuram District through Personalized Reinforcement Learning–Based Training", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.d14-d20, January-2026, Available :http://www.jetir.org/papers/JETIR2601303.pdf

"Advancing Competency Development among Life Insurance Advisors in Villupuram District through Personalized Reinforcement Learning–Based Training", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppd14-d20, January-2026, Available at : http://www.jetir.org/papers/JETIR2601303.pdf