Direct marketing campaigns in retail banking with the use of deep learning and random forests

作者：

Highlights：

• Credit products are the crucial part of business of retail banks.

• Time series approach - events in database are processed as time series.

• Machine learning system selects most promising customers for a marketing campaign.

• Transactional data is used by the proposed system to extract significant patterns.

摘要

•Credit products are the crucial part of business of retail banks.•Time series approach - events in database are processed as time series.•Machine learning system selects most promising customers for a marketing campaign.•Transactional data is used by the proposed system to extract significant patterns.

论文关键词：Consumer credit,Retail banking,Direct marketing,Marketing campaigns,Database marketing,Random forest,Deep learning,Deep belief networks,Data mining,Time series,Feature selection,Boruta algorithm

论文评审过程：Received 27 September 2018, Revised 3 April 2019, Accepted 14 May 2019, Available online 15 May 2019, Version of Record 28 May 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.05.020