Explainable machine learning for retirement product suitability: Balancing risk, fees, and outcome sufficiency

The growing sophistication in retirement products has led to many challenges including increased the chances of mis-selling, inefficiency of fees, and insufficient income to sustain retirees. The paper designs an explainable-AI-first (XAI-first) framework which incorporates fee drag, sequence-of-returns risk, and user risk capacity and tolerance to enhance the suitability of the retirement products. Based on tabular machine learning algorithms, including Gradient Boosting Machines (GBM) and XGBoost, this study produces local and global interpretability in terms of SHapley Additive exPlanations (SHAP). Monte Carlo simulations are used to estimate income adequacy in diverse market conditions whereas fairness audits disaggregate the results in terms of age and income to examine distributional equity. Based on 20 peer-reviewed articles covering the areas of governance, actuarial machine learning, ESG-driven financial policy, and data-driven compliance systems, this study is offering a reproducible and auditable methodology to minimize mis-selling and increase compliance transparency. The findings prove that the incorporation of XAI practices into the retirement planning can trade the adequacy of returns and the exposure to risk in such a way that these two aspects can be interpreted by regulators and advisors. The model can be used to create responsible financial plans that ensure consumer safety and sustainable retirement benefits. Finally, the framework helps to make AI-based financial innovation responsible and align the fairness of algorithms with long-term adequacy and compliance with policy.