Auditing plays a crucial role in ensuring the quality of financial reports. To achieve this, technological tools such as artificial intelligence (AI), blockchain, deep learning, and machine learning are essential in facilitating auditors' work. Thus, we aimed to conduct a systematic literature review (SLR) to consolidate key findings and identify future research opportunities regarding the contributions of AI, blockchain, deep learning, and machine learning to financial auditing. We collected articles from the Web of Science (WoS) database without a predefined time frame. Four search terms were used: ⅰ) "Artificial intelligence, " "financial, " and "audit"; ⅱ) "blockchain, " "financial, " and "audit"; ⅲ) "machine learning, " "financial, " and "audit"; and ⅳ) "deep learning, " "financial, " and "audit." The search resulted in 172 articles. After removing duplicates, inaccessible papers, and those unrelated to the study's objective, we obtained sixty-two relevant studies. The findings indicated that technological tools enable the analysis of large volumes of data, contributing to improved financial reporting quality. As a result, reports become more reliable, enabling users to make more accurate decisions. These technologies also help mitigate the risk of fraud. However, challenges remain, as some professionals perceive potential risks, such as errors in parameter settings. Additionally, the high costs associated with these tools can hinder adoption, particularly for smaller firms. Our findings contribute to the auditing field by highlighting the advantages of integrating these technologies into auditors' daily work and their role in enhancing stakeholder trust. For researchers, this study provides a consolidated theoretical foundation on AI's contributions to auditing, facilitating the identification of trends, challenges, and future opportunities. Even with recent developments, significant gaps remain in the literature regarding the interaction of emerging technologies, such as blockchain, deep learning, machine learning, and artificial intelligence, with auditor independence. These gaps are especially noticeable when it comes to how these technologies affect corporate governance structures, audit transparency, and tone management in sustainability reporting. Additionally, little is known about the obstacles that small and medium-sized businesses (SMEs) encounter when implementing these advances, particularly those related to technical expertise and infrastructure. Additionally, few empirical studies compare technology-driven audits with those conducted by human auditors, particularly in terms of stakeholder trust, operational efficiency, and the application of professional judgment. Finally, the ethical, legal, and bias-mitigation concerns surrounding the use of technology in auditing remain inadequately addressed by existing theoretical frameworks.
Citation: Dryelle Laiana De Jesus Silva Dos Santos, Geovane Camilo Dos Santos. Technological convergence in financial auditing: A systematic literature review[J]. Data Science in Finance and Economics, 2025, 5(4): 440-465. doi: 10.3934/DSFE.2025018
Auditing plays a crucial role in ensuring the quality of financial reports. To achieve this, technological tools such as artificial intelligence (AI), blockchain, deep learning, and machine learning are essential in facilitating auditors' work. Thus, we aimed to conduct a systematic literature review (SLR) to consolidate key findings and identify future research opportunities regarding the contributions of AI, blockchain, deep learning, and machine learning to financial auditing. We collected articles from the Web of Science (WoS) database without a predefined time frame. Four search terms were used: ⅰ) "Artificial intelligence, " "financial, " and "audit"; ⅱ) "blockchain, " "financial, " and "audit"; ⅲ) "machine learning, " "financial, " and "audit"; and ⅳ) "deep learning, " "financial, " and "audit." The search resulted in 172 articles. After removing duplicates, inaccessible papers, and those unrelated to the study's objective, we obtained sixty-two relevant studies. The findings indicated that technological tools enable the analysis of large volumes of data, contributing to improved financial reporting quality. As a result, reports become more reliable, enabling users to make more accurate decisions. These technologies also help mitigate the risk of fraud. However, challenges remain, as some professionals perceive potential risks, such as errors in parameter settings. Additionally, the high costs associated with these tools can hinder adoption, particularly for smaller firms. Our findings contribute to the auditing field by highlighting the advantages of integrating these technologies into auditors' daily work and their role in enhancing stakeholder trust. For researchers, this study provides a consolidated theoretical foundation on AI's contributions to auditing, facilitating the identification of trends, challenges, and future opportunities. Even with recent developments, significant gaps remain in the literature regarding the interaction of emerging technologies, such as blockchain, deep learning, machine learning, and artificial intelligence, with auditor independence. These gaps are especially noticeable when it comes to how these technologies affect corporate governance structures, audit transparency, and tone management in sustainability reporting. Additionally, little is known about the obstacles that small and medium-sized businesses (SMEs) encounter when implementing these advances, particularly those related to technical expertise and infrastructure. Additionally, few empirical studies compare technology-driven audits with those conducted by human auditors, particularly in terms of stakeholder trust, operational efficiency, and the application of professional judgment. Finally, the ethical, legal, and bias-mitigation concerns surrounding the use of technology in auditing remain inadequately addressed by existing theoretical frameworks.
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Dryelle Laiana De Jesus Silva Dos Santos, Geovane Camilo Dos Santos. Technological convergence in financial auditing: A systematic literature review[J]. Data Science in Finance and Economics, 2025, 5(4): 440-465. doi: 10.3934/DSFE.2025018
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