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, . and Januar Eko Prasetio, . (2025) From AI Policy to Financial Reporting Outcomes: AI Ecosystem, AI Investment, and Accrual Quality in Leading ASEAN-6 Banking Firm (2020 - 2024). International Journal of Applied Business & International Management (IJABIM), 10 (3). pp. 600-614. ISSN 2621-2862

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Abstract

This study examines the relationship between national AI ecosystem factors and accrual quality in leading ASEAN-6 banking firms over the period 2020–2024. Using a balanced panel dataset of 140 firm-year observations from 28 banks, accrual quality is measured through the loan loss provision (LLP) model. The study employs a panel fixed effects regression with Driscoll-Kraay standard errors to control for cross-sectional dependence and serial correlation. The model demonstrates a strong explanatory power with a within R² of 0.3405 and is jointly significant (F(3,132) = 22.71, p < 0.001). The results show that regulatory sandbox existence significantly reduces accrual quality scores (Coef. = −0.0343; t = −5.5493; p < 0.001), indicating improved reporting quality. In contrast, AI governance readiness has a significant but opposite effect (Coef. = 0.0094; t = 2.3585; p = 0.0183), suggesting increased accrual deviation. AI investment intensity is found to be statistically insignificant (Coef. = −10.8135; p = 0.4381). Overall, the findings highlight that regulatory sandbox serves as the most robust institutional mechanism linking AI ecosystem development to improved financial reporting quality in ASEAN banking systems.

Item Type: Article
Additional Information: Steven Getha Pradessa (Penulis - 142220117) Januar Eko Prasetio (Pembimbing)
Uncontrolled Keywords: Accrual Quality; Regulatory Sandbox; AI Governance; AI Investment;Loan Loss Provision Model
Subjek: H Social Sciences > HB Economic Theory
Divisions: Fakultas Ekonomi dan Bisnis > (S1) Akuntansi
Depositing User: Bayu Pambudi
Date Deposited: 18 May 2026 04:38
Last Modified: 18 May 2026 04:38
URI: http://eprints.upnyk.ac.id/id/eprint/48368

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