AI-Driven Personalized Pension Planner: A Comprehensive Review and Proposed Framework

In the present study, we review the application of advanced technological tools incorporating artificial intelligence to provide utilized, individualized retirement and pension planning. The traditional pension calculator and financial advisor systems still rely primarily on predetermined, mathematical formulae, previous years' averages and general retirement planning assumptions made by actuaries. While these basic methods are helpful, they are limited in that they do not provide accurate or appropriate predictions regarding an individual's current and future assets and income during retirement and with respect to any changes in the economy, along with an individual's behavior over time and changing risk tolerance. Therefore, the predictions resulting from these basic approaches will likely be inaccurate and unable to accommodate changes within a financial environment. Recently developed machine learning methods provide an alternative means of building a more accurate and user-friendly pension plan. In this paper, the author presents a consolidated overview of the latest developments in machine learning, including multiple regression models, risk profile clustering, probability- based forecasting techniques, and portfolio optimization. Through the creation of an AI-based system that incorporates all of the above-mentioned methodologies, these systems can adapt to the increasing nonlinear nature of the financial environment; develop predictive capabilities based upon changing data sources; and provide users with personal recommendations based upon their specific requirements. The analytical framework's rationale and logical organization are captured through a successive set of mathematical equations and formulae. Within this framework, each element contributes toward higherquality predictions and better-informed choices, and the use of mathematical equations and formulae provides an explanation for why machine learning (ML) models provide superior performance compared to traditional heuristics in other areas, including identifying risk classifications; projecting income; and creating an optimal asset allocation strategy.