预测使用基于树的算法在银行服务中接受机器人咨询
Witold Orzeszko1, Dariusz Piotrowski2
1Department of Applied Informatics and Mathematics in Economics, Nicolaus Copernicus University, Toruń, Poland.
PloS one
|May 6, 2024
概括
银行可以利用现有数据预测客户对机器人咨询服务的兴趣. 这允许有针对性的营销,提高促销效率,推动创新银行技术的采用.
科学领域:
- * 金融技术和银行业中的人工智能.
- * 消费者行为和采用数字服务.
背景情况:
- * 银行业正在采用机器人咨询服务,以提高效率和改善客户服务.
- * 机器人咨询利用来自各种通信道的客户数据.
- *预测消费者兴趣对于有效的促销策略至关重要.
研究的目的:
- * 构建和评估预测模型,用于消费者接受银行机器人咨询服务.
- * 确定现有银行数据是否可以识别对机器人咨询感兴趣的客户.
- * 提高数字银行解决方案营销活动的有效性.
主要方法:
- *使用机器学习算法 (决策树,集合模型) 开发基于树的预测模型.
- *利用了关于波兰银行业人工智能调查数据.
- *预测因素包括社会人口统计,数字技术态度,银行经验和对银行的信任.
主要成果:
- * 构建模型有效地预测消费者对机器人咨询服务的接受度.
- *确定了影响客户对数字银行工具态度的关键因素.
- * 证明了机器学习在理解消费者采用模式方面的实用性.
结论:
- *预测模型可以准确预测客户采用机器人咨询的情况.
- *利用客户数据可以提高针对创新银行服务的有针对性的营销.
- *这项研究为银行实施人工智能驱动的金融咨询提供了有价值的见解.
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