<![CDATA[This study aims to design a Microsoft Excel-based financial distress prediction tool that can be used as a basis for credit decision-making by creditors. The tool is equipped with Visual Basic for Applications (VBA) to enhance its functionality and ease of use. Financial distress prediction serves as an early warning system to assess prospective debtors’ ability to meet their financial obligations before credit is granted. The tool incorporates several financial distress analysis models, namely the Grover model and the Zmijewski model. The study was conducted on LQ45 index companies listed on the Indonesia Stock Exchange in 2020 (pre-COVID-19) and in 2023 (post-pandemic). The results show that the developed prediction tool is capable of providing comprehensive, informative, and easily accessible financial analysis through a simple user interface. The tool is expected to assist creditors in conducting more accurate and objective financial analyses, thereby minimizing the risk of default.]]>
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