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; Januar Eko Prasetio

doi:10.32535/ijabim.v10i3.4455

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, Januar Eko Prasetio

Abstract

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.

Keywords

Accrual Quality, Regulatory Sandbox, AI Governance, AI Investment, Loan Loss Provision Model

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References

Anderson, J., Bholat, D., Gharbawi, M., & Thew, O. (2021). The impact of COVID-19 on artificial intelligence in banking. Bruegel-Blogs, NA-NA. https://go.gale.com/ps/i.do?p=AONE&sw=w&issn=&v=2.1&it=r&id=GALE%7CA659197095&sid=googleScholar&linkaccess=fulltext

Arner, D. W., Barberis, J. N., & Buckley, R. P. (2015). The Evolution of Fintech: A New Post-Crisis Paradigm? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2676553

Bangguiyac, G. D., & Castañeda, M. O. (2025). Entrepreneurial Leadership and Business Performance of MSMEs: The Mediating Role of Government Support. International Journal of Applied Business and International Management, 10(2), 313–329. https://doi.org/10.32535/ijabim.v10i2.4079

Beatty, A., & Liao, S. (2014). Financial accounting in the banking industry: A review of the empirical literature. Journal of Accounting and Economics, 58(2–3), 339–383. https://doi.org/10.1016/j.jacceco.2014.08.009

Bromberg, L., Godwin, A., & Ramsay, I. (2017). Fintech Sandboxes: Achieving a Balance between Regulation and Innovation.

Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company. https://psycnet.apa.org/record/2014-07087-000

Chichernea, D. C., Holder, A. D., & Petkevich, A. (2015). Does return dispersion explain the accrual and investment anomalies? Journal of Accounting and Economics, 60(1), 133–148. https://doi.org/10.1016/j.jacceco.2014.08.001

Clarke, T., Chelliah, J., & Pattinson, E. (2017). National innovation systems in the Asia Pacific: A comparative analysis. Innovation in the Asia Pacific: From Manufacturing to the Knowledge Economy, 3(1), 119–143. https://doi.org/10.1007/978-981-10-5895-0_6

Dechow, P. M., & Dichev, I. D. (2002). The Quality of Accruals and Earnings: The Role of Accrual Estimation Errors. The Accounting Review, 77(s-1), 35–59. https://doi.org/10.2308/accr.2002.77.s-1.35

Francis, J., LaFond, R., Olsson, P. M., & Schipper, K. (2004). Costs of Equity and Earnings Attributes. The Accounting Review, 79(4), 967–1010. https://doi.org/10.2308/accr.2004.79.4.967

Goyal, K., Garg, M., & Malik, S. (2025). Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: a hybrid approach (SEM-ANN). Future Business Journal 2025 11:1, 11(1), 44-. https://doi.org/10.1186/s43093-025-00464-3

Han, M. S., & Chen, W. (2021). Determinants of eco-innovation adoption of small and medium enterprises: An empirical analysis in Myanmar. Technological Forecasting and Social Change, 173, 121146. https://doi.org/10.1016/j.techfore.2021.121146

Hankins, E., Fuentes, P., Livia, N., Grau, M. G., & Rahim, S. (2023). Oxford Insights.

King, Brett. (2018). Bank 4.0 : banking everywhere, never at a bank. Marshall Cavendish Business.

Kortum, S., & Lerner, J. (2000). Assessing the Contribution of Venture Capital to Innovation. The RAND Journal of Economics, 31(4), 674. https://doi.org/10.2307/2696354

Liao, C. H., San, Z., & Tsang, A. (2024). Corporate governance reforms and voluntary disclosure: International evidence on management earnings forecasts. Journal of International Accounting, Auditing and Taxation, 54, 100602. https://doi.org/10.1016/j.intaccaudtax.2024.100602

Madan, R., & Ashok, M. (2023). AI adoption and diffusion in public administration: A systematic literature review and future research agenda. Government Information Quarterly, 40(1), 101774. https://doi.org/10.1016/j.giq.2022.101774

Mishrif, A., & Khan, A. (2023). Technology adoption as survival strategy for small and medium enterprises during COVID-19. Journal of Innovation and Entrepreneurship 2023 12:1, 12(1), 53-. https://doi.org/10.1186/s13731-023-00317-9

Noreen, U., Shafique, A., Ahmed, Z., & Ashfaq, M. (2023). Banking 4.0: Artificial Intelligence (AI) in Banking Industry & Consumer’s Perspective. Sustainability (Switzerland), 15(4). https://doi.org/10.3390/su15043682

OECD. (2025). Governing with Artificial Intelligence: The State of Play and Way Forward in Core Government Functions. OECD Publishing. https://doi.org/10.1787/795de142-en

Pradnyawati, S. O., Yuliantari, N. K. A., Wedayanti, N. M. E., & Yunita, N. L. P. (2024). The Analysis of Financial Ratios, Good Corporate Governance, Reward, and Asymmetric Information in Earnings Management of Manufacturing Companies in Indonesia. International Journal of Applied Business and International Management, 9(3), 416–432. https://doi.org/10.32535/ijabim.v9i3.3466

Sabihaini, Kurniawan, A., Eko Prasetio, J., & Rusdiyanto. (2024). Environmental analysis and impact on green business strategy and performance in SMEs post the Covid-19 pandemic. Cogent Economics and Finance, 12(1). https://doi.org/10.1080/23322039.2024.2330428

Taeihagh, A. (2021). Governance of artificial intelligence. Policy and Society, 40(2), 137–157. https://doi.org/10.1080/14494035.2021.1928377

Xie, L., Peng, Z., & Tong, X. (2026). AI strategy, earnings management, and corporate fraud: Evidence from listed firms in China. Economic Modelling, 156, 107460. https://doi.org/10.1016/j.econmod.2025.107460

Yiming, Z., & Manansala, L. (2024). The Impact of Digital Transformation on the Innovation Capacity of Chinese-Listed Firms: The Role of Government Subsidies. International Journal of Applied Business and International Management, 9(2), 31–46. https://doi.org/10.32535/ijabim.v9i2.3393

DOI: https://doi.org/10.32535/ijabim.v10i3.4455