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This study discusses the application of artificial intelligence in accounting analytics in developing economies, where technological change intersects with institutional and infrastructural constraints. AI technologies—automation, machine learning, and real-time auditing—are transforming accounting through improved efficiency, accuracy, and predictive ability. The study identifies key facilitators of adoption, such as digital maturity, scalable cloud-based solutions, and an innovation culture. Yet it also recognizes ongoing challenges, including regulatory fragmentation, low human capital, and nascent data governance frameworks. Country case studies on Kenya, India, and Brazil demonstrate the concrete gains of AI in improving tax compliance, fraud detection, and financial inclusion. Conversely, the study urges the importance of ethical frameworks, public–private coordination, and adaptive policies to inform implementation. Future pathways—like explainable AI, integration with blockchain, and AI-as-a-Service—are also discussed, providing practical avenues for responsible and scalable adoption. Collectively, the study provides a strategic roadmap for utilizing AI to foster transparency and resilience in financial systems throughout the developing world.

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Artificial Intelligence and Big Data Analytics for Sustainable Accounting in Emerging Economies: Contemporary Approaches and Future Prospects

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  • 发表日期：

    2026年

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  • Ammar Zakaria Salem,
  • Raed Abdelhaq,
  • Bahaa Awwad

摘要

This study discusses the application of artificial intelligence in accounting analytics in developing economies, where technological change intersects with institutional and infrastructural constraints. AI technologies—automation, machine learning, and real-time auditing—are transforming accounting through improved efficiency, accuracy, and predictive ability. The study identifies key facilitators of adoption, such as digital maturity, scalable cloud-based solutions, and an innovation culture. Yet it also recognizes ongoing challenges, including regulatory fragmentation, low human capital, and nascent data governance frameworks. Country case studies on Kenya, India, and Brazil demonstrate the concrete gains of AI in improving tax compliance, fraud detection, and financial inclusion. Conversely, the study urges the importance of ethical frameworks, public–private coordination, and adaptive policies to inform implementation. Future pathways—like explainable AI, integration with blockchain, and AI-as-a-Service—are also discussed, providing practical avenues for responsible and scalable adoption. Collectively, the study provides a strategic roadmap for utilizing AI to foster transparency and resilience in financial systems throughout the developing world.