Abstract
Integrating Generative AI (GenAI) and advanced machine learning techniques into financial health predictions represents a revolutionary approach to financial technology. While prior research has incorporated machine learning and artificial intelligence into financial analysis, GenAI has not yet been incorporated into financial models. Our comprehensive experimental study aims to bridge this gap by harnessing the advanced capabilities of Generative AI to improve predictive accuracy and model robustness. The distinctive contribution of this study lies in its utilization of Generative AI, which offers novel insights and methodologies that traditional machine-learning techniques do not provide. A key discovery of this study is the alignment of Generative AI with quantitative models, revealing the potential to identify fraud and financial difficulties that stakeholders should consider before making investment decisions. Moreover, the study proposes that a mixed-method approach could be beneficial for future research in risk measurement. These unique and novel findings highlight that traditional methods would not have been able to uncover such insights. This research provides robust and interpretable financial assessments and contributes valuable knowledge to financial technology, showcasing the innovative application of Generative AI in financial health predictions.
Published Version
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