<![CDATA[The increasing complexity of accounting–tax differences and risk-based tax administration has strengthened the need for more structured approaches to tax risk assessment. Financial statement analysis is widely used to identify potential tax risk signals; however, manual interpretation often produces inconsistent outcomes because financial indicators are highly interconnected and difficult to evaluate systematically. This study examines whether financial statement indicators can identify potential tax risk and whether Artificial Intelligence-assisted analysis improves the quality of tax risk identification compared with manual analysis. Using a quasi-experimental design with a difference-in-differences approach, this study evaluated the analytical performance of 130 Diploma III Tax students analyzing the financial statements of 25 Indonesian publicly listed companies during 2020–2024 through manual and Artificial Intelligence-assisted stages. Tax risk identification performance was assessed using a standardized score based on effective tax rate, book-tax differences, profitability ratios, and cash flow gaps. The findings indicate that Artificial Intelligence-assisted analysis was associated with more consistent, structured, and efficient interpretation of financial statements compared with manual analysis. This study contributes to tax accounting literature by integrating Artificial Intelligence-assisted financial statement analysis into a structured tax risk assessment framework.]]>
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