在人工神经网络下的数字经济发展中的信贷治理分析
Zhenzhen Huang1, Zhiming Xu2, Xiangyu Wang1
1School of Economics and Trade, Hunan University, Changsha, 410079, China.
Heliyon
|December 6, 2024
概括
本研究介绍了使用物联网 (IoT) 数据进行可靠的信用评估的改进的长期短期记忆模型. 这种新的方法在数字经济中提高了信贷治理的准确性和效率.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 金融技术 金融技术
背景情况:
- 数字经济需要先进的信用治理解决方案.
- 传统的信用评估方法与数字互动的复杂性作斗争.
- 物联网 (IoT) 技术为改善信用评估提供了丰富的数据源.
研究的目的:
- 提出一种利用物联网数据的创新信贷治理方法.
- 提高信用评估对金融,商业和社会互动的可靠性.
- 提高信用评估模型的性能和效率.
主要方法:
- 将物联网 (IoT) 数据集成到信贷治理中.
- 开发一个改进的长期短期记忆 (LSTM) 神经网络模型.
- 实施适应性机制,用于实时数据监控和参数调整.
主要成果:
- 拟议的模型实现了高性能指标:0.9准确度,0.91F1值和0.94曲线下的面积 (AUC).
- 与现有方法相比,证明了更高的分类准确性和性能.
- 保持高效的培训时间,表明实际可用性.
结论:
- 开发的基于物联网的LSTM模型为数字经济中的信贷治理提供了一个有希望的方法.
- 适应机制增强了模型的稳定性和实时响应能力.
- 这些发现表明在信用风险评估和管理方面有很大的应用潜力.
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