使用机器学习技术预测贷款批准的堆积集体方法
Kunchakara Raja Sekhar1, Shaiku Shahida Saheb2
1VIT-AP School of Business, VIT-AP University.
Journal of visualized experiments : JoVE
|October 13, 2025
概括
本研究介绍了一种机器学习模型,用于在数字贷款中准确预测贷款批准. 开发的堆叠组合模型实现了98%的准确性,提高了金融包容性和信贷可访问性.
科学领域:
- 金融科技和数字贷款.
- 金融中的机器学习
- 信用风险评估 信用风险评估
背景情况:
- 数字贷款和金融科技创新正在改变全球金融包容性和信贷可用性.
- 点对点 (P2P) 和数字贷款平台利用人工智能和机器学习等技术进行贷款批准.
- 数字贷款的挑战包括算法风险,客户信任,金融排斥和监管差距.
研究的目的:
- 检查数字贷款和金融科技的不断变化的格局.
- 为准确的贷款批准预测提出一个强大的机器学习方法.
- 通过先进的分析来解决数字贷款生态系统中的挑战.
主要方法:
- 一个堆叠集团机器学习模型被开发用于贷款批准预测.
- 数据预处理涉及列车测试分区,探索性分析和标签编码.
- 合奏模型使用XGBoost作为一个元学习者,并使用梯度提升,高效梯度提升,AdaBoost和额外树作为基础学习者.
主要成果:
- 堆叠组合模型在预测贷款批准方面达到98%的高准确度.
- 影响贷款批准的关键因素包括资产,收入和CIBIL分数.
- 与传统方法相比,该模型表现出卓越的性能和通用性.
结论:
- 拟议的机器学习模型为自动化,数据驱动的信贷决策提供了强大的工具.
- 这种方法可以提高数字贷款行业的效率和准确性.
- 该研究强调了先进的ML技术在改善金融包容性和信贷可访问性方面的潜力.
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