Predicting Retirement Outcome Sufficiency with behavior-Aware ML: A Comparative Study of Contribution Nudges and Glide-Path Adjustments

Retirement planning is an issue that majority of the population especially the low and middle-income earners are worried about and there is no better time than now to establish viable and dynamic solutions that will see to it that the sufficient retirement is realized. Most of the traditional retirement savings plans like target-date funds (TDFs) employ the traditional glide-path plans to minimize risk in investing as the retiree approaches the retirement age. Of course, these strategies are effective in some cases, however they do not usually take into account instability of income and the non-standard conditions of individuals with variable income. This disparity is observed especially in the group of lower and middle-income earners who are more vulnerable to various economic recessions like loss of employment or medical accidents. Thus, there can be certain serious inconsistencies of these groups being willing to retire despite saving on a regular basis. 

New developments in machine learning (ML) and artificial intelligence (AI) offer an opportunity to change the way retirement planning is done and make it more personal and dynamic. The retirement plans will also be more responsive to the changes in the income and will be customized to the needs of each individual saver with the assistance of these technologies. Examples of cases where AI and ML-based solutions are applicable include dynamically adjusting the contribution rates in response to changes in income or dynamically rebalancing investment portfolios in response to changes in income. Such adaptive plans can increase the retirement sufficiency of people with unpredictable financial journeys, but little empirical study has been carried out to contrast the effectiveness of traditional glide-path plans with AI-driven, behavior-conscious nudging.