Revolutionizing Financial Health Predictions: The Integration of GenAI and Advanced Machine Learning Techniques

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.

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Publication Details

Journal
Journal of Applied Business and Economics
Published
2024-12-31
DOI
https://doi.org/10.33423/jabe.v26i6.7434
Primary Topic
Financial Distress and Bankruptcy Prediction
Type
article
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0.00
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article

Revolutionizing Financial Health Predictions: The Integration of GenAI and Advanced Machine Learning Techniques

Karina Kasztelnik, Steven Campbell
Journal of Applied Business and Economics
Financial Distress and Bankruptcy Prediction
article

Revolutionizing Financial Health Predictions: The Integration of GenAI and Advanced Machine Learning Techniques

Karina Kasztelnik, Steven Campbell
article en

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.

Journal of Applied Business and EconomicsVol. 26(6)
Tennessee State University (US)
Openalex Percentile: Top 59%
Financial Distress and Bankruptcy Prediction
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Revolutionizing Financial Health Predictions: The Integration of GenAI and Advanced Machine Learning Techniques — Karina Kasztelnik, Steven Campbell · Journal of Applied Business and Economics (2024) | TGRS Research Map | TGRS