用户对RBI批准的P2P数字贷款应用程序的看法:一种NLP,机器学习和深度学习方法
Kunchakara Raja Sekhar1, Shaiku Shahida Saheb1
1VIT-AP School of Business, VIT-AP University, Amaravati, AP, India.
Frontiers in artificial intelligence
|January 28, 2026
概括
印度的数字贷款平台在金融包容方面表现有前景,但也面临着挑战. 用户反强调了改善界面,透明度和客户支持的需求,以增强信任和采用.
科学领域:
- 金融科技创新和数字金融服务.
- 数字贷款中的消费者行为.
- 机器学习在金融技术分析中的应用.
背景情况:
- 数字贷款或替代贷款通过金融科技平台提供可访问的贷款,与传统银行业务形成对比.
- 印度的快速增长与金融包容性倡议有关.
- 该行业面临着欺诈,缺乏透明度和消费者不满等持续挑战.
研究的目的:
- 分析消费者对印度RBI批准的P2P数字贷款应用程序的看法.
- 根据用户反,确定关键的优点和弱点.
- 评估用户对数字贷款平台的整体满意度.
主要方法:
- 分析了来自印度储备银行批准的7个数字贷款平台的15,408条用户评论.
- 自然语言处理 (NLP),主题建模和机器学习 (ML) 技术的应用.
- 使用深度学习模型 (VGG16) 和整体ML (XGBoost,CatBoost,LightGBM) 进行主题识别和绩效评估.
主要成果:
- 主题建模揭示了11个关键主题;情绪分析显示55%是积极的,41%是消极的,4%是中立的反.
- 优势:高效的贷款支付,提款和EMI支付.
- 缺点:接口设计,拒绝原因不明,登录问题. 印度MoneyMart和i2iFunding领先于满意度; 5Paisa和Lendbox落后.
- 深度学习和整体ML模型实现了高预测精度 (高达0.88).
结论:
- 数字贷款平台有助于金融包容,但需要UI/UX增强.
- 增加贷款决策的透明度和强大的客户支持至关重要.
- 解决这些问题将促进更大的信任和长期采用数字贷款服务.
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