Optimization of credit marketing strategy based on customer demand and RNN
发布时间：2026-04-02点击次数：
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DOI码：10.1186/s40537-026-01421-9
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发表刊物：Journal of Big Data
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关键字：Deep neural network；Generative adversarial network；Recurrent neural network；Classification improvement algorithm；Credit usage prediction；Precision credit marketing
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摘要：Optimizing credit marketing strategies not only improves banks’economic performance but also meets the evolving needs of the market economy. Toachieve this goal, this paper puts forward an integrated optimization strategy from two perspectives: credit usage prediction and credit interval prediction.For credit usage prediction, the paper applies decision trees to extract business rules and filters them accordingly. Meanwhile, a credit usage predictionmodel is built using Deep Neural Networks, where Recurrent Neural Networks are used to capture time-series features and Categorical Boosting modelare used to process non-time-series feaures. Experimental results show that the credit usage prediction model achieves an accuracy of 95.2% to 97.%and an Area Under the Receiver Operating Characteristic Curve ranging from 0.93 to 0.95 on two datasets, significantly outperforming baseline modelsThe credit interval prediction model reaches a maximum Mean Absolute Error of 3.651, a minimum Mean Squared Eror of 23.250, and a coefficient of-determination greater than 0.896. In addition, the integrated strategy based on both models shows strong adaptability across diferent scenarios. Thesefindings suggest that the proposed models demonstrate reliable predictive performance and can provide analytical support for optimizing credit marketing strategies.
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是否译文：否
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发表时间：2026-04-02
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收录刊物：SCI、EI
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李远哲
职务：Professor
主要任职：
个人信息
- 博士生导师
硕士生导师 - 教师英文名称：Yuanzhe
- 教师拼音名称：liyuanzhe
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- 所在单位：碳中和研究院
- 职务：Professor
- 学历：博士研究生毕业
- 办公地点：中国矿业大学(文昌校区) 教三楼 碳中和研究院
- 性别：男
- 联系方式：yuanzhe.li@cumt.edu.cn
- 学位：哲学博士学位
- 毕业院校：Nanyang Technological University
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- 2025当选：江苏特聘教授
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- 2017-09-18新加坡人力部注册安全官（WSHO）
- 2025-09-01江苏特聘教授
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