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This is a Quantitative Study study.

October 5, 2025

Developing an innovative corporate financial risk management system using artificial intelligence and big data

Key Points

The AI-driven model achieved a remarkable accuracy of 97% in identifying financial risks, demonstrating its efficiency.
Key performance indicators like precision (96%) and recall (94%) indicate its superiority over traditional financial risk management methods.
The model utilizes big data and machine learning to enhance predictive modeling, adapting continuously to the changing financial landscape.
This advancement in financial risk management contributes significantly to the field, improving decision-making in corporate finance.

Abstract

Companies face rising complexity in their financial risk management practices because market fluctuations and large amounts of available data have increased in the modern global finance environment. Researchers have presented CB-ADT as an innovative financial risk management approach which merges DT technology with boosting methods to enhance system adaptability while improving accuracy in constantly transforming financial markets. Training occurs through utilization of big data containing historical finance records and marketplace patterns together with present economic metric signals. The CB-ADT model is developed and trained using Python software to process large volumes of historical financial data. The system demonstrated superior performance in key metrics, such as accuracy (97%), precision (96%), recall (94%) and F1-score (96%) showcasing its ability to better identify and mitigate financial risks. This AI-driven approach marks a significant advancement over conventional methods, offering more robust and adaptive solutions for corporate financial risk management. These high-performance indicators highlight the model’s effectiveness in distinguishing between high-risk and low-risk financial scenarios, allowing organizations to make more informed financial decisions. The model's adaptive learning capability enables it to continuously refine its predictions based on new data, ensuring long-term relevance and applicability in an ever-changing financial landscape. This AI-driven approach marks a significant advancement over conventional methods, offering more robust and adaptive solutions for corporate financial risk management. It enhances risk assessment by automating data processing and analysis, reducing human error, and increasing decision-making efficiency. Through machine learning methods financial institutions obtain the capability to detect potential threats ahead of time while maximizing their risk defense approaches. The findings of this research contribute to the growing field of AI-powered financial risk management, paving the way for further advancements in predictive modeling and intelligent decision-making frameworks.

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