A neural network bionic algorithm-based approach to modeling cost and efficiency management behaviors of financial BPOs from a biomechanical perspective

This study integrates principles of biomechanics to develop a neural network-based behavior modeling approach for enhancing cost and efficiency management in financial business process outsourcing (BPO). Drawing inspiration from the adaptive and efficient characteristics of biological systems, we mo...

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Veröffentlicht in:	Molecular & cellular biomechanics Jg. 22; H. 4; S. 861
Hauptverfasser:	Jiang, Juncong, Xie, Weifeng, Yang, Yiru
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	10.03.2025
ISSN:	1556-5297, 1556-5300
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	This study integrates principles of biomechanics to develop a neural network-based behavior modeling approach for enhancing cost and efficiency management in financial business process outsourcing (BPO). Drawing inspiration from the adaptive and efficient characteristics of biological systems, we model the financial BPO landscape using neural networks on cloud computing platforms. This approach mirrors the interconnected and dynamic nature of biomechanical networks, enabling proactive adaptation and optimization in financial environments. By utilizing financial SMOTE algorithms and integrating network storage infrastructure, data resources, management platforms, and financial service applications, we construct a comprehensive decision-support architecture. This model achieves a significant reduction in financial costs by 60% and enhances the adaptability and operational efficiency of financial management systems. By conceptualizing financial systems as dynamic, interactive networks, our method provides innovative solutions for mitigating operational risks and enhancing enterprise resilience in competitive markets. The incorporation of biomechanical concepts into financial modeling offers novel insights into optimizing resource allocation and improving system adaptability within complex financial ecosystems.
ISSN:	1556-5297 1556-5300
DOI:	10.62617/mcb861