<![CDATA[Digital transformation in financial systems has encouraged companies to implement automation to improve the speed, effectiveness, accuracy, and efficiency of business processes, particularly in financial and invoice management. However, manual processes still often cause delays and data entry errors. This study aims to analyze the implementation of end-to-end hyperautomation integrating Robotic Process Automation (RPA), Artificial Intelligence (AI), Document Intelligence, and other supporting technologies in financial and invoice automation processes, while identifying research gaps in its implementation. This study employs a Systematic Literature Review (SLR) method using the PRISMA approach on peer-reviewed empirical articles published between 2021 and 2026. Out of 45 identified articles, 20 were selected for further analysis. The findings reveal that studies on end-to-end hyperautomation implementation in financial and invoice processes remain very limited, while most existing studies still focus on partial automation technologies, such as RPA, OCR, or AI separately. These findings highlight a significant research gap in the comprehensive integration of hyperautomation, providing opportunities for developing more integrated automation models to enhance efficiency, effectiveness, and minimize human error in business processes. ]]>
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