Prediction Analysis of Potential Fraudulent Financial Statement Risks Using the Fraud Score Model (In Mining Companies Listed on the Indonesian Stock Exchange 2018-2020)

Abstract

This quantitative research project aimed to assess fraud risk in the financial statements of mining sector companies listed on the Indonesia Stock Exchange between 2018 and 2020. The study focused on measuring and predicting the likelihood of fraudulent financial reporting during this period using specific independent variables. Accrual quality was evaluated using RSST (Relative Signed Surprises of Total Accruals) as a proxy. Additionally, financial performance indicators, such as changes in accounts receivable, inventory, cash sales, and income, were analyzed to understand the financial health of the selected firms. The dependent variable was the prediction of fraudulent financial statements. Through statistical analysis and data modeling, the study aimed to identify patterns and trends that could indicate potential fraudulent reporting in the sampled mining companies' financial statements. A targeted sampling method was used, selecting 22 mining companies with available financial data from 2018 to 2020, categorized into two groups: 14 affiliated with big four KAP (Kantor Akuntan Publik) and 8 with non-big four KAP. Data analysis methods included descriptive statistics and lap cubes tests, helping summarize and compare the two groups in terms of their ability to mitigate the risk of fraudulent financial statements. The research findings revealed that companies associated with big four KAP firms were more effective at reducing fraud in their financial statements compared to non-big four counterparts. This underscores the importance of robust financial oversight and audit practices in the mining sector for maintaining investor confidence and market stability. Continuous research and monitoring of financial reporting practices are vital for transparency within the Indonesian Stock Exchange.