Enabling the 1 1 Promise AI Enabled Fairness for Indonesia Retail IPO Allocation

The regulation's structural vulnerability: Indonesia's November 2025 IPO rule reserves 50% of allocations for retail investors in a single pool, but well-resourced participants can circumvent this by distributing bids across multiple synthetic retail accounts that each appear compliant individually, allowing them to capture the retail quota while the economic benefit flows to institutional actors.

Existing detection methods are insufficient: Standard concentration heuristics and rule-based detection alone cannot identify nominee networks operating within the retail pool, meaning the statutory guarantee of equal access exists "procedurally" on paper but not in practice without additional enforcement mechanisms.

AI-based solutions use existing data: The paper demonstrates that nominee networks can be identified using only eleven statutory fields already collected by Indonesia's e-IPO infrastructure, requiring no new surveillance powers or expansion of personal data collection—making algorithmic enforcement both feasible and privacy-conscious.

Genuine inclusion requires active enforcement: The authors argue that when regulators prioritize authentic market access over administrative convenience, algorithmic tools become necessary rather than optional; without them, formal quotas fail to deliver their intended economic benefit to actual retail households.

Global South regulators can lead on responsible AI: The paper positions Indonesia's approach as a model showing how emerging market regulators can implement fairness-focused AI enforcement that protects vulnerable populations without requiring invasive surveillance or new data collection infrastructure.