华东师范大学学报（自然科学版） >

2020 , Vol. 2020 >Issue 5: 167 - 178

DOI: https://doi.org/10.3969/j.issn.1000-5641.202091016

数据中台应用

基于聚合支付平台交易数据的商户流失预测

  • 徐一文 ,
  • 黎潇阳 ,
  • 董启文 ,
  • 钱卫宁 ,
  • 周昉
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  • 华东师范大学 数据科学与工程学院, 上海 200062

收稿日期: 2020-08-16

  网络出版日期: 2020-09-24

基金资助

国家自然科学基金(61902127); 上海市自然科学基金(19ZR1415700)

收起

Merchant churn prediction based on transaction data of aggregate payment platform

  • XU Yiwen ,
  • LI Xiaoyang ,
  • DONG Qiwen ,
  • QIAN Weining ,
  • ZHOU Fang
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  • School of Data Science and Engineering, East China Normal University, Shanghai 200062, China

Received date: 2020-08-16

  Online published: 2020-09-24

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摘要

在聚合支付领域, 为了减少聚合支付平台的运营成本、提高平台利润率, 要解决的一个关键问题是确保平台中达到较低的商户流失率. 本文所关注的是聚合支付平台的商户流失预测问题, 目标是帮助平台及时挽回可能流失的客户. 基于交易流水数据和商户基本信息, 本文提出了与商户流失密切相关的特征, 采用多种传统机器学习模型进行流失预测. 考虑到商户的交易流水数据具有时序性, 增加了基于LSTM的多种时间序列模型来建模. 在真实数据集上的实验结果表明手动提取的特征具有一定的预测能力, 结果具有可解释性; 采用时间序列模型能够较好地学习到数据的时序特征, 从而进一步提升预测结果.

关键词： 流失预测; 特征工程; 时间序列模型

本文引用格式

徐一文 , 黎潇阳 , 董启文 , 钱卫宁 , 周昉 . 基于聚合支付平台交易数据的商户流失预测[J]. 华东师范大学学报（自然科学版）, 2020 , 2020(5) : 167 -178 . DOI: 10.3969/j.issn.1000-5641.202091016

Abstract

In the field of aggregate payments, ensuring a low dropout rate of merchants on the platform is a key issue to reduce the overall platform operating cost and increase profit. This study focuses on the prediction of merchant churn for aggregate payment platforms and aims to help the platform reactivate potential churn merchants. The paper proposes a series of features that are highly relevant to merchant churn and applies a variety of traditional machine learning models for prediction. Given that the data analyzed contains sequential information, the study, moreover, applies LSTM-based techniques to address the prediction problem. Experimental results on a real dataset show that the proposed features have a certain predictive ability and the results are interpretable. And, the LSTM-based approaches are capable of capturing the timing characteristics in the data and further improve prediction results.

Key words： churn prediction; feature engineering; time series models

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