Big Data-Driven Optimization of Retirement Solutions: Integrating Data Governance and AI for Secure Policy Management
DOI:
https://doi.org/10.70179/jx0mvq88Keywords:
Big Data Analytics, Retirement Solutions, AI in Policy Management, Data Governance, Secure Data Architecture, Predictive Analytics in Retirement Planning, Artificial Intelligence in Finance, Policy Optimization, Data-Driven Decision Making, Compliance and Security, Pension Technology, Financial Risk Modeling, Digital Transformation in Retirement Services, Machine Learning for Policy Personalization, Data Privacy in Financial Services.Abstract
Pension systems are essential for providing financial support to individuals after they retire from work and are key components of the social contracts supporting civil society at large. Despite this necessity and importance, there exists a gap in applied economic literature on the research and development of financial solution products supporting pension systems. In this essay, we begin to explore this gap. In particular, we investigate and develop financial models of retirement portfolio allocation and longevity risk management in the presence of guaranteed returns and increases, as well as protected minimum levels for retirement income, together with the optimal choices of product, capital allocation, and premium levels. We explore these problems in significant financial depth and detail using the tools of applied mathematics, finance, and economics, such as stochastic dynamic programming and Lagrangian optimization, parametric numerical optimization, and numerical simulations of the pension problem to generate prediction results. We apply our models to the design, pricing, and optimization of retirement portfolio annuitization solutions, pension investment product allocation, and longevity risk management come retirement time, for the key actors in pension systems, namely individual pension fund participants, insurance and pension fund companies, and their regulators.
We illustrate the power and versatility of our models through concrete numerical simulations of the classical model settings, as well as possible extensions that take into account the specific mechanisms of various retirement solutions currently sold on the market. These numerical simulations allow us to generate comparative results that can help inform the important policy question of how to improve the functioning of private pension systems in society, as well as specific guidelines for the development and pricing of innovative retirement financial solutions.
