Harnessing Big Data and AI for Predictive Insights: Assessing Bankruptcy Risk in Indonesian Stocks

Maureen Marsenne, Tubagus Ismail, Muhamad Taqi and Imam Abu Hanifah

Data and Metadata, 2024, vol. 3, .622

Abstract: Introduction: This research aims to investigate the use of financial Big Data and artificial intelligence (AI) in predicting the bankruptcy risk of companies listed on the Indonesia Stock Exchange (BEI), with the Altman Z-Score model as the main framework. Objective: In this research, an intervening variable in the form of financial data quality is introduced to assess the role of mediation in increasing the accuracy of bankruptcy predictions.. Method: The research method used is quantitative with the analytical method used is Structural Equation Modeling Partial Least Squares (SEM-PLS), which allows analysis of the relationship between independent variables (Big Data and AI), intervening variables (quality of financial data), and dependent variables (bankruptcy risk prediction). Result: The research results show that the integration of financial Big Data and AI significantly increases the accuracy of company bankruptcy risk predictions on the IDX, with the quality of financial data acting as an intervening variable that strengthens this relationship. The influence of Big Data and AI on bankruptcy prediction through the quality of financial data has also been proven to provide more precise and faster results compared to the conventional Altman Z-Score model. Conclusion: These findings confirm that the quality of financial data is a key factor that must be considered in optimizing bankruptcy predictions in the capital market. This research has implications for the development of financial technology (Fintech) and risk management strategies in public companies, especially in identifying bankruptcy risks more effectively by utilizing the latest technology.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:3:y:2024:i::p:.622:id:1056294dm2024622

DOI: 10.56294/dm2024.622

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