AI helps ensure fair retail IPO share distribution in Indonesia
Enabling the 1 1 Promise AI Enabled Fairness for Indonesia Retail IPO Allocation
Computational Engineering, Finance, and Science
Summary
Indonesia has a rule that half of shares in stock market IPOs must go to everyday small investors, not big companies. But some well-funded investors try to sneakily get more shares by pretending to be many small bidders. The authors show how AI can spot these fake networks using existing data, making sure the rule truly helps regular people. This approach works without needing new privacy-invading data or extra legal powers.
What this means in practice
- •For financial regulators: Detect networks of fake retail accounts bidding in IPOs to enforce fair share distribution under Indonesia's new rules.
- •For financial compliance teams: Implement algorithmic checks using existing investor data fields to identify coordinated retail bid manipulations before IPO allotment closes.
Authors
Anchal Gupta, Gleb Papyshev, James T Kwok
Abstract
Indonesia OJK Circular, effective November 2025, requires that half of every IPO pooled allocation go to retail investors. Uniquely amongst major capital markets, the regulation places all non-institutional participants, regardless of wealth, into a single competing pool rather than separating them by monetary thresholds. The rule is structurally sound; however, enforcement and measurement of effectiveness require methods beyond rule-based detection alone. Well-resourced participants operating within the retail pool can distribute bids across networks of synthetic retail accounts, each individually compliant, allowing them to capture the retail quota without triggering standard concentration heuristics. Without a mechanism to detect such networks before allotment closes, the statutory guarantee of equal access becomes procedurally hollow: the quota is formally reserved for retail households while the economic benefit flows elsewhere. We present a framework for identifying such nominee networks using eleven statutory fields already collected by Indonesia e-IPO infrastructure, requiring no new surveillance powers or personal data. The paper shows that when a Global South regulator prioritizes genuine inclusion over administrative convenience, algorithmic enforcement becomes both feasible and necessary.