一种新的整体方法来估计银行电话营销的能力
Wei Guo1, Yao Yao1, Lihua Liu1
1College of Innovation & Entrepreneurship, Shanghai Jianqiao University, Shanghai, 201306, Shanghai, China.
Scientific reports
|November 27, 2023
概括
本研究引入了先进的混合机器学习模型,以预测银行电话营销的成功. 电磁场优化人工神经网络 (EFO-ANN) 模型在预测客户存款订阅方面表现出最高的准确性.
科学领域:
- * 机器学习 * 机器学习
- * 人工智能 * 人工智能
- * 计算金融学
背景情况:
- *了解银行电话营销业绩对于经济成功至关重要.
- *机器学习模型越来越多地用于分析财务数据.
- *以前的模型在准确预测电话营销中的客户行为方面存在局限性.
研究的目的:
- * 开发和评估复杂的混合模型来估计银行电话营销成功率.
- * 用metaheuristic算法训练的四个不同的人工神经网络 (ANN) 模型的性能进行比较.
- *确定最有效的模型来预测客户订阅长期存款产品.
主要方法:
- *利用庞大的,公开可用的葡萄牙银行客户信息数据集.
- *应用四个ANN训练的元启发算法:电磁场优化 (EFO),未来搜索算法 (FSA),和搜索算法 (HSA),和社会滑雪司机 (SSD).
- *根据19个条件参数预测客户订阅的可能性.
主要成果:
- *所有四种神经元启发式混合模型都显示出在分析和预测订阅模式方面具有显著的潜力.
- * EFO-ANN模型获得了最高的准确性,曲线下的面积 (AUC) 约为0.80.
- * FSA-ANN,SSD-ANN和HSA-ANN分别以0.7714,0.7663和0.7160的AUC进行了跟进.
- * EFO-ANN模型的表现优于之前研究中的几种传统模型.
结论:
- *神经-元启发式混合模型对于银行电话营销绩效分析具有很高的能力.
- * EFO-ANN模型是预测存款订阅概率的可靠和有效工具.
- * 银行机构可以在实践中实施EFO-ANN模型,以加强远程营销策略.
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