<![CDATA[Informal workers dominate Indonesia’s labour market but remain largely excluded from formal pension systems due to low and volatile incomes, raising concerns about old-age income security. The central question addressed is whether an ultra-micro digital pension scheme can generate meaningful retirement benefits for low-income informal workers and whether artificial intelligence (AI) can enhance contribution effectiveness under income uncertainty. A simulation-based framework is employed using official income statistics, combining deterministic and Monte Carlo simulations over a 25-year accumulation period and a 15-year payout phase. Three contribution designs are evaluated: a fixed ultra-micro nominal contribution, a flexible income-based contribution equal to 3% of earnings, and an income-based scheme augmented by an AI-assisted top-up mechanism. Fixed nominal contributions produce limited replacement rates of around 8%, while income-based contributions increase replacement rates to approximately 15%. The integration of AI-assisted contribution optimisation further raises replacement rates to about 19–20% and shifts the distribution of outcomes upward, improving downside protection under income volatility. Although the resulting benefits remain below conventional adequacy benchmarks, the findings demonstrate that ultra-micro digital pensions are financially feasible as complementary retirement instruments and that AI-enabled contribution mechanisms add measurable value in highly informal, low-income labour market settings]]>
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