Proactive Financial Wellness Coaching Via Generative AI And Reinforcement Learning-Driven Behavioral Nudging

Authors

  • Kali Prasad Chiruvelli Author

DOI:

https://doi.org/10.64252/84rvwx41

Abstract

The financial services industry is undergoing significant change due to the integration of artificial intelligence, which is fundamentally reshaping traditional advisory models and customer engagement. Modern financial wellness coaching systems leverage the convergence of

generative AI and reinforcement learning (RL) to provide proactive, individualized interventions that go beyond traditional advisory services. These advanced systems address major gaps in financial guidance access, especially for underserved populations who face significant obstacles to traditional money management services.

The proposed architecture integrates sophisticated natural language generation with adaptive learning mechanisms to personalize financial materials, budget templates, and strategies in real-time based on individual customer profiles and circumstances. Reinforcement learning agents optimize the timing, content, and distribution of these interventions by analyzing behavioral patterns and financial outcomes, leading to a progressive improvement in effectiveness. The technical implementation uses a distributed microservice framework to support high-volume concurrent sessions with minimal delay. Advanced security measures, including

homomorphic encryption, federated learning, and differential privacy, protect sensitive financial data while enabling personal recommendations. While challenges such as data privacy, algorithm bias, and regulatory compliance exist, the future implications of this technology suggest it can democratize financial guidance and contribute to overall economic stability.

Downloads

Download data is not yet available.

Downloads

Published

2025-10-16

Issue

Section

Articles

How to Cite

Proactive Financial Wellness Coaching Via Generative AI And Reinforcement Learning-Driven Behavioral Nudging. (2025). International Journal of Environmental Sciences, 5952-5956. https://doi.org/10.64252/84rvwx41