Transformasi Akuntansi Konsolidasi: Peran AI dalam Efisiensi dan Akurasi Pelaporan
Main Article Content
Abstract
Proses akuntansi konsolidasi pada entitas multinasional sering kali menghadapi hambatan akibat kompleksitas eliminasi transaksi timbal balik yang rentan terhadap kesalahan manual dan inefisiensi waktu. Penelitian ini bertujuan mengeksplorasi peran strategis Artificial Intelligence (AI) dan Machine Learning (ML) dalam mentransformasi efisiensi serta akurasi pelaporan konsolidasi sesuai standar IFRS 10 dengan menerapkan metode Sequential Explanatory Mixed Method. Temuan penelitian mengonfirmasi bahwa adopsi AI secara signifikan meningkatkan kinerja konsolidasi melalui otomatisasi proses rekonsiliasi yang mampu mereduksi cycle time sebesar 47,8%, yakni dari rata-rata 14,2 hari menjadi 7,4 hari. Selain itu, integrasi teknologi ini meningkatkan akurasi pelaporan sebesar 83,2% dengan menekan error rate dari 12,5% menjadi 2,1%. Namun, hasil Moderated Regression Analysis (MRA) menunjukkan bahwa dampak positif tersebut dimoderasi secara negatif oleh faktor risiko, termasuk isu AI Black Box, bias algoritma, dan keterbatasan audit trail. Tantangan operasional seperti infrastruktur sistem warisan (legacy systems) dan "utang teknis" juga teridentifikasi sebagai penghambat utama yang dapat menggerus manfaat efisiensi. Oleh karena itu, penelitian menyimpulkan bahwa optimalisasi AI memerlukan implementasi kerangka AI Governance yang ketat serta pergeseran paradigma kompetensi akuntan menuju data science guna menjamin integritas dan akuntabilitas pelaporan keuangan.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Al-Abbas, M. (2024). The Transparency of AI Algorithms in Financial Reporting: Navigating the Black Box. Journal of Digital Auditing and Risk Management, 12(1), 45–62.
Al-Harrasi, S., Al-Salti, S. , Al-Hashmi, S., & Al-Manthari, K. (2021). Cybercrime and Cybersecurity on Fintech: A Systematic Literature Review. Journal of Management and Operations, 3(2), 239–251.
Al-Rashed, M. (2022). Regulatory Compliance Risks in AI-Driven Auditing: A Managerial Perspective. International Journal of Digital Accounting, 8(2), 112–128.
Al-Sayed, F. , Hassan, O. , & El-Khadrawy, M. (2021). The Impact of Artificial Intelligence on the Efficiency of Financial Closing and Reporting Processes in Multinational Companies. Journal of Accounting Technology, 15(2), 112–135.
Brown, R., Cooper, S. , & Evans, L. (2023). Implementation Challenges of AI in Financial Reporting: A Study of Data Integrity and System Integration. The Review of Accounting Information Systems, 27(1), 45–68.
Chen, H., & Lee, S. (2023). Phased implementation strategies for cloud-based accounting systems. Journal of Cybersecurity and Financial Management, 15(2), 45–60.
Chen, J. , & Li, M. (2023). The Impact of Cloud-Based Accounting Systems on Operational Efficiency: Evidence from Chinese SMEs. Journal of Accounting Technology, 15(2), 45–62.
Chen, L. , & Wang, Y. (2023). AI Adoption and KPI Shifts in Tech-SMEs.
Chen, S. , & Wang, H. (2022). The Role of Automation in Improving the Accuracy of Financial Data. Journal of Digital Accounting, 9(1), 50–65.
Chen, Y. , & Wang, Z. (2024). Machine Learning Algorithms for Intercompany Transaction Matching: Reducing Manual Error in Consolidation. Asian Journal of Advanced Accounting, 38(3), 201–225.
Creswell, J. (2014). Research Design: Qualitative, Quantitative. Canadian Center of Science and Education.
Deloitte. (2023). The Future of Finance: How AI is Reshaping Consolidation and Closing Cycles. Deloitte Global Press.
Garcia, P. , & Rodriguez, M. (2024). Algorithmic Bias in Automated Accounting: A Study of Ethical and Governance Issues in Financial Consolidation. Journal of Business Ethics and AI, 5(4), 500–518.
García-Sánchez, I. M. (2022). The Impact of Ethical AI on Corporate Performance and Stakeholder Trust. Ethics and Information Technology, 24(2), 15–30.
Gupta, A. , & Sharma, K. (2020). Automating Elimination Entries: Using Robotic Process Automation and Machine Learning in Group Accounts. International Journal of Accounting and Finance Research, 9(1), 1–19.
Hadi, A. , & Prasetyo, B. (2024). Technical Debt and AI Scalability in Multinational Corporations. Asian Journal of Technology Management, 17(1), 54–71.
Hadid, W., & Al-Sayed, M. (2021a). Management accountants and strategic management accounting: The role of organizational culture and information systems. Management Accounting Research, 50. https://doi.org/10.1016/j.mar.2020.100725
Hadid, W., & Al-Sayed, M. (2021b). Management accountants and strategic management accounting: The role of organizational culture and information systems. Management Accounting Research, 50. https://doi.org/10.1016/j.mar.2020.100725
Hair, J. F. , Black, W. C., Babin, B. J. , & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.
Hassan, F. , et al. (2024). Operational Cost Optimization through AI-Driven Analytics in Financial Services. Journal of Financial Innovation, 10(2), 210–230.
International Accounting Standards Board (IASB). (2021). IFRS 10: Consolidated Financial Statements. IFRS Foundation.
Johnson, T. , & Miller, A. (2020). Transparency and Traceability: The Challenge of the Audit Trail in AI-Driven Consolidation Systems. Journal of Audit and Assurance Practice, 32(4), 310–332.
Jones, K. , & Smith, C. (2022). Artificial Intelligence and the Evolution of the Accountant’s Skillset: A Qualitative Study. Accounting Education: An International Journal, 31(6), 650–675.
Kim, Y. , Lee, M. , & Park, S. (2014). Shopping value orientation : Conceptualization and measurement. Journal of Business Research, 67(1), 2884–2890.
KPMG. (2020). Consolidation in the Digital Age: Addressing Complexity with Automation. KPMG Global Publication.
Kumar, P. (2021). Cognitive Fatigue and Human Error in Accounting: The Role of AI in Reducing Clerk Burden. International Journal of Accounting Information Systems, 42, 100–115.
Li, J. , & Wang, S. (2024). Machine Learning vs. Manual Sampling: A Comparative Study on Material Misstatement Detection. Accounting Horizons, 38(1), 145–167.
Lopez-Espinoza, N., & Garcia-Sanchez, I. M. (2023). Sustainable development goals and the financial performance of SMEs: The role of environmental, social, and governance reporting. Journal of Small Business Management, 61(2), 522–555.
Miller, D. (2023). Real-Time Reporting and Strategic Decision-Making: The Role of Cloud Accounting. Journal of Management Accounting, 18(3), 201–215.
Miller, T. (2022). Predictive Analytics and Value Creation: How AI Optimizes Capital Allocation. Journal of Corporate Finance, 72, 102–120.
Muller, L. , et al. (2023). Data Governance as a Moderator of AI Performance in Corporate Reporting. Journal of Information Systems, 37(2), 89–110.
PricewaterhouseCoopers (PwC). (2022). Global Reporting Trends: Navigating Regulatory Changes and Data Volume in Group Reporting. PwC Research.
Rahman, A. , & Aziz, N. (2025). From Data Preparer to Strategic Advisor: The Evolution of the Finance Manager in the AI Era. Global Accounting Review, 19(1), 77–94.
Roberts, K., & Clark, L. (2021). Scalability and Labor Productivity: AI Implementation in High-Volume Transactional Environments. Management Science Quarterly, 66(3), 412–435.
Smith, A. , & Jones, B. (2023). From Desktop to Cloud: The Evolution of Accounting Software in the Digital Age. Journal of Accounting and Finance Research, 30(1), 56–71.
Smith, A. , et al. (2021). Legacy Systems and the Barriers to AI Implementation in Operational Risk Management. Technology in Society, 64, 101–118.
Smith, J., & Wright, R. (2022). Robotic Process Automation and the Future of the Monthly Close. Journal of Emerging Technologies in Accounting, 19(2), 55–72.
Smith, R. , & Rupp, W. (2021). The strategic benefits of cloud-based accounting for SMEs: Efficiency, scalability, and cost reduction. Journal of Financial Management and Analysis, 34(2), 55–70.
Sutton, N., & Brown, D. A. (2021). Beyond the decision to ally: Constraints on adapting to emergent control risks. Management Accounting Research, 52. https://doi.org/10.1016/j.mar.2021.100756
Tan, H., & Lee, S. (2025). The Moderating Role of Cyber Risk in AI Performance: Evidence from Decision Support Systems. MIS Quarterly, 49(1), 134–155.
Tan, L. , Wong, H. , & Lim, P. (2019). AI Governance and Control Mechanisms: Ensuring Regulatory Compliance in Automated Consolidation. Journal of Financial Regulation and Compliance, 27(5), 550–570.
Wu, C. , & Li, F. (2021). Data Governance Framework for Machine Learning Application in Corporate Financial Processes. Journal of Data Science in Finance, 10(2), 88–105.
Zhang, L. , & Moore, K. (2023). Deep Learning in Real-Time Audit Monitoring: Reducing Error Detection Rates. Contemporary Accounting Research, 40(4), 2100–2125.
Zhao, Y. , et al. (2023). Compliance Risk and Algorithmic Bias in Banking: A Barrier to AI Optimization. Journal of Financial Regulation and Compliance, 280–298.