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

Abstract

Indonesia’s OJK Circular, effective November 2025, requires that half of every IPO’s 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; enforcement and measuring effectiveness require measures 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, to capture the retail quota undetected by 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 whilst the economic benefit flows elsewhere. We present a framework identifying such nominee networks using eleven statutory fields already collected by Indonesia’s e-IPO infrastructure, requiring no new surveillance powers or personal data. The framework maps account connections to identify hidden networks (Louvain), learns behavioural patterns signalling coordination by mining association rules (Apriori), and scores each network’s likelihood of being a coordinated evasion ring (Random Forest). Evaluated on synthetic data bids (n = 154, 000) across 25 IPO offerings, calibrated against published e-IPO statistics and public disclosures, the framework serves two functions. Pre-allotment exclusion of nominee networks redistributes shares to genuine retail households, reducing allocation inequality by 63 to 66% per offering (Gini). As supervisory intelligence, the model correctly separates nominee networks from genuine retail households in approximately 98 out of 100 cases (weighted AUROC ≈ 0.98) even as actors shift tactics across offerings. False positives for kinship clusters are suppressed by design. This paper demonstrates that when a Global South regulator prioritises genuine inclusion over administrative convenience, algorithmic enforcement is not merely useful but demonstrably effective.