Retirement Portfolio Allocation Optimization Using Machine Learning with XAI

This research discusses the important and complex issues of retirement planning in India, where the majority of workers must rely on their own savings due to rising life expectancy, low returns from traditional debt instruments, and high inflation, while only a small portion of the workforce receives benefits from government pension plans. This study uses Explainable AI (XAI) in conjunction with sophisticated machine learning techniques to create a comprehensive framework for retirement portfolio allocation optimization in order to offer a solid answer. The suggested methodology uses Modern Portfolio Theory (MPT), Monte Carlo simulations, and XAI tools like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) to improve transparency, reliability, and user confidence in financial decision-making. Historical financial data is used to create diverse and individualized investment portfolios based on individual risk tolerances, financial goals, and investment horizons. The combination of data science and financial modelling ensures adaptation to India's changing economic climate, allowing investors to properly balance risk and reward. By combining existing techniques such as MPT and the Efficient Frontier with modern machine learning algorithms, the framework offers both robust performance and access. XAI provides interpretable insights that enable investors to grasp the reasoning behind portfolio recommendations, boosting trust and informed decision-making. Finally, this method seeks to provide Indian retirees with the tools and techniques required to attain financial independence and security in the face of complicated socioeconomic obstacles.