Signal Processing and Machine Learning for the Sustainability of the Italian Social Security System: Evidence from ISTAT Pension Data

The long-run sustainability of pay-as-you-go pension systems crucially depends on the dynamic balance between social-security contributions paid by the working population and benefits paid to retirees. In Italy, the National Social Security Institute (INPS) manages the core of the public system, whose financial equilibrium is increasingly challenged by demographic aging, labor market fragility, and macroeconomic shocks. In this paper, in line with the aims of the Special Issue “Signal Processing and Machine Learning in Real-Life Processes”, we reinterpret the Italian pension system as a complex stochastic signal-processing problem. Using the most recent data published in the Annuario Statistico Italiano 2024 highlighting by ISTAT—with a focus on Protection and Social Security—we construct a set of time series describing contributions, benefits, coverage ratios and pension amounts, both at the national and territorial level. On this basis, we compare classical time-series models and a recurrent neural network with Long Short-Term Memory (LSTM) architecture for multi-step forecasting of the main aggregates. The signal-processing perspective allows us to disentangle trend, cyclical and shock components, while machine learning provides flexible nonlinear forecasting tools capable of capturing structural breaks such as the COVID-19 crisis. Our empirical results suggest that (i) pension expenditure remains high and persistent as a share of GDP; (ii) the contribution coverage ratio improved in 2022 but remains below the pre-pandemic level; and (iii) regional heterogeneity in the per-capita pension deficit is substantial and stable over time, with persistent imbalances in Southern regions and Islands. Finally, we perform a scenario analysis combining LSTM-based forecasts with demographic and labor market hypotheses, and we quantify the impact of alternative policy measures on the future pension deficit signal. The proposed framework, which integrates permutation-based inference, signal decomposition and deep learning, provides a reproducible template for the real-time monitoring of pension sustainability using official open data.