%0 Journal Article
%T AI-Enhanced Behavioral Retirement Planning: A Conceptual Framework
%A Divya Srivastava
%J Open Access Library Journal
%V 12
%N 11
%P 1-11
%@ 2333-9721
%D 2025
%I Open Access Library
%R 10.4236/oalib.1114347
%X This paper introduces a novel AI-enhanced retirement planning platform that
integrates behavioral economics principles with advanced machine learning
techniques to optimize financial decision-making. Traditional financial planning
models, while numerically sound, often fail to consider the cognitive biases and
behavioral patterns that influence individual choices. Our system addresses this
gap by incorporating mechanisms that counteract biases such as loss aversion,
present bias, and overconfidence¡ªcommon deterrents to long-term financial
planning. The proposed architecture comprises modular layers for data integration,
AI-driven analysis, and a responsive user interface that delivers personalized
behavioral nudges. The platform leverages deep reinforcement learning, natural
language processing using GPT-4, and predictive healthcare simulations to create a
highly personalized and adaptive retirement planning experience. This work
represents a significant advancement toward personalized, equitable, and
behaviorally informed financial technologies.
%K Artificial Intelligence (AI)
%K Generative AI
%K GPT Models
%K Reinforcement Learning
%K Behavioral Economics
%K Retirement Planning
%K Healthcare Simulation
%K Conversational Interfaces
%K Voice Assistants
%U http://www.oalib.com/paper/6876464