From AI Policy to Financial Reporting Outcomes: AI Ecosystem, AI Investment, and Accrual Quality in Leading ASEAN-6 Banking Firm (2020 - 2024) - Steven Getha Pradessa; Januar Eko Prasetio (AIBPM Publisher, 2025)
This study investigates whether national AI institutional support and investment intensity correlate with accrual quality among leading banking firms across six ASEAN economies (i.e., Indonesia, Malaysia, Singapore, Thailand, Viet Nam, the Philippines) over the period 2020–2024. Using a balanced panel of 140 firm-year observations drawn from 28 banks, accrual quality is measured through the Beatty Liao (2014) loan loss provision model, while three country-level AI Ecosystem variables are examined: regulatory sandbox adoption (X1), AI governance readiness (X2), and AI venture capital investment intensity (X3). A panel fixed effects regression with Driscoll-Kraay standard errors is employed to account for country-level unobserved heterogeneity, cross-sectional dependence and serial autocorrelation. The model achieves a within-country R² of 0.3405 with F(3, 132) = 22.71 (p 0.0010). Regulatory sandbox existence significantly reduces accrual quality scores (Coef. = ?0.0343, p 0.0010), supporting H1. The AI Governance Index produces a statistically significant but directionally contrary effect (Coef. = 0.0094, p = 0.0183), contradicting H2. AI investment intensity yields a statistically insignificant result (P-value = 0.4381), failing to support H3. Findings suggest that operational AI governance infrastructure is the primary institutional channel through which AI Ecosystem translates into improved bank financial reporting quality across ASEAN-6.