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通过改进的LSTM算法设计农村小额信贷模型和信用风险评估
Xia Gao1, Xiaoqian Yang1, Yuchen Zhao1
1Business School, University of Jinan, Jinan, Shandong, China.
本研究介绍了一个优化的农村小额信贷风险模型,使用自组织的长短期记忆 (LSTM) 算法. 新模型提高了准确性和稳定性,改善了金融机构的信贷风险管理.
科学领域:
- * 金融技术 * 金融技术
- * 数据科学数据科学
- * 农村经济学 农村经济学
背景情况:
- * 传统的信贷风险模型与农村贷款的独特特征作斗争.
- *农村小额信贷对经济发展和农民收入至关重要,但面临评估挑战.
- *现有的模型缺乏适应性,无法在不同农村环境中评估风险.
研究的目的:
- * 设计一个优化的农村小额信贷风险评估模型.
- * 提高农村贷款风险评估的准确性和稳定性.
- * 为金融机构提供加强的风险管理支持.
主要方法:
- * 采用了改进的长短期记忆 (LSTM) 算法,结合了自我组织的方法.
- *利用LSTM对顺序数据建模的能力来捕获长期依赖关系.
- *将拟议的模型与传统的信用风险评估方法进行比较.
主要成果:
- * 自组织的LSTM模型表现出比传统方法更高的准确性和稳定性.
- *该模型有效控制农村小额信贷场景中的信用违约风险.
- * 集成实时监控和预警功能,有助于及时决策.
结论:
- * 开发的模型为农村地区的金融机构提供了强大的风险管理支持.
- * 它有助于农村经济的稳定发展,并推进金融技术.
- *需要对各种农村环境进行进一步的验证,以确保可持续的应用.
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