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<p>Artificial intelligence offers flexible solutions for managing financial risk in businesses, which helps people make better, more proactive decisions. This work presents a large-scale, data-driven, concentric deep learning model for forecasting financial risks and evaluating their effects on organizational sustainability and development. The model uses historical financial policy and risk behavior data from a variety of sources, such as Kaggle datasets and World Bank financial indicators, to identify the most important risk factors and their relative importance. The best criteria are used to optimize financial allocations, and learning from past successful policies makes sure that under-supplied resources are protected from risk. With deep concentric learning, the model may continue to improve its predictions by uncovering subtle and complex patterns that other models would miss. The experimental evaluation shows that the model can make very accurate predictions, indicating it can help with long-term financial stability and strategic planning. This method increases risk anticipation by using sustainability and linear growth trends in the prediction process. It also follows rules such as the EU AI Act, which ensures that AI is used fairly and openly. The suggested framework provides businesses and banks with a useful way to make better decisions, use their resources more efficiently, and reduce risk at all levels of management.</p>

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Design and Optimization of Enterprise Financial Risk Prediction Model Based on Machine Learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月30日

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  • Tong Tian

摘要

Artificial intelligence offers flexible solutions for managing financial risk in businesses, which helps people make better, more proactive decisions. This work presents a large-scale, data-driven, concentric deep learning model for forecasting financial risks and evaluating their effects on organizational sustainability and development. The model uses historical financial policy and risk behavior data from a variety of sources, such as Kaggle datasets and World Bank financial indicators, to identify the most important risk factors and their relative importance. The best criteria are used to optimize financial allocations, and learning from past successful policies makes sure that under-supplied resources are protected from risk. With deep concentric learning, the model may continue to improve its predictions by uncovering subtle and complex patterns that other models would miss. The experimental evaluation shows that the model can make very accurate predictions, indicating it can help with long-term financial stability and strategic planning. This method increases risk anticipation by using sustainability and linear growth trends in the prediction process. It also follows rules such as the EU AI Act, which ensures that AI is used fairly and openly. The suggested framework provides businesses and banks with a useful way to make better decisions, use their resources more efficiently, and reduce risk at all levels of management.