Bank Marketing Prediction Based on XGBoost

Research Article
Open access

Bank Marketing Prediction Based on XGBoost

Yuqi Wang 1*
  • 1 Hunan First Normal University    
  • *corresponding author yukiyukiwang2@gmail.com
AEMPS Vol.193
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-201-0
ISBN (Online): 978-1-80590-202-7

Abstract

Under the dual challenges of fintech evolution and digital transformation, commercial banks face increasing limitations in traditional marketing prediction methods, which struggle with static customer profiling, low data utilization, and poor adaptability to real-time demands. This study addresses these gaps by proposing an XGBoost-based predictive framework to enhance precision marketing and risk-adjusted returns in banking scenarios. We integrate multidimensional features, including static attributes (e.g., age, occupation) and dynamic behavioral indicators (e.g., consumer confidence index, Euribor rates), to overcome the unidimensional profiling limitations of conventional approaches. Methodologically, XGBoost demonstrates superior performance through three innovations: firstly efficient handling of high-dimensional sparse data via parallel computing, reducing marginal processing costs while improving prediction accuracy (89% accuracy, 90% AUC). Secondly, mitigation of information asymmetry by synthesizing transactional, social, and macroeconomic features (e.g., employment variation rate, housing loans). Comparative analyses against five benchmark models (GBDT, Random Forest, Decision Tree, Logistic Regression, Bagging) confirm XGBoost’s dominance in AUC and F1-score, validating its capacity to resolve nonlinear interactions and temporal sensitivity in marketing campaigns. The model’s scalability enables cost-effective targeting. This research contributes to both algorithmic optimization in financial marketing and operational decision-making frameworks, though limitations persist in handling extreme class imbalances. Future work will explore hybrid architectures combining XGBoost with deep learning for cross-channel behavioral modeling.

Keywords:

XGBoost, Precision Marketing, Banking Analytics, Risk-Reward Optimization, Fintech Applications

Wang,Y. (2025). Bank Marketing Prediction Based on XGBoost. Advances in Economics, Management and Political Sciences,193,14-23.
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References

[1]. Yang, G., (2018) Analysis of Problems and Countermeasures in Traditional Commercial Bank Marketing under the Background of Internet Finance[J]. Journal of Financial Research, 45(3): 112-120.

[2]. Yang, L., Liu, Y., Zhang, S., et al., (2020) Exploration of Machine Learning Applications in Commercial Banks[J]. Journal of China Fintech, 8(2): 45-58.

[3]. Ge, Y. (2023). Research on the Innovation of Marketing Models in Chinese Commercial Banks in the Big Data Era. In Proceedings of the 5th International Conference on Fintech and Banking Innovation, 123-135. Singapore: Springer.

[4]. Chen, G. M., & Sun, X. L., (2023). Application research of XGBoost fusion model in bank customer churn prediction. Computer Knowledge and Technology, 13, 55–57.

[5]. Ji, C. Y., (2018) Research on imbalanced data classification and its application in bank marketing. [Journal Name in Chinese], 5, 55–57.

[6]. Sun, S. Q., (2023) 2023 China Banking Marketing Digitalization Industry Research Report. TMT Financial Group, iResearch Inc.

[7]. Fang, L. Z., & Cao, X. Y. (2023) Research on Intelligent Data Cleaning and Preprocessing Algorithms in University Informatization Systems. In Proceedings of the 2023 International Conference on Educational Data Mining, 45-58, Tokyo, Japan: IEEE.

[8]. Song, K., (2022) Research on Bank Marketing Data Analysis and Application Based on Mixed Sampling and Ensemble Learning. Master’s thesis, Guizhou University.

[9]. L. Yang, P. Sun, B. Yuan, Q. Long, D. Xiao., (2023) Implementation of Campus Recruitment Data Analysis and Visualization System Using Python. In: Proceedings of the 2023 International Conference on Educational Technology and Computer Science (ETCS 2023). IEEE, 2023: 234-239. DOI: 10.1109/ETCS.2023.00045

[10]. Shao W., (2022) Credit Risk Assessment and Prediction in P2P Lending Platforms: A Decision Tree-Based Approach". In: Proceedings of the 2022 International Conference on Financial Technology and Risk Management (FTRM 2022). IEEE, 2022: 112-117. DOI: 10.1109/FTRM.2022.00027

Cite this article

Wang,Y. (2025). Bank Marketing Prediction Based on XGBoost. Advances in Economics, Management and Political Sciences,193,14-23.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of ICEMGD 2025 Symposium: Innovating in Management and Economic Development

ISBN：978-1-80590-201-0(Print) / 978-1-80590-202-7(Online)
Editor：Florian Marcel Nuţă Nuţă, Ahsan Ali Ashraf
Conference website: https://2025.icemgd.org/Lahore.html
Conference date: 23 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.193
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Yang, G., (2018) Analysis of Problems and Countermeasures in Traditional Commercial Bank Marketing under the Background of Internet Finance[J]. Journal of Financial Research, 45(3): 112-120.

[2]. Yang, L., Liu, Y., Zhang, S., et al., (2020) Exploration of Machine Learning Applications in Commercial Banks[J]. Journal of China Fintech, 8(2): 45-58.

[3]. Ge, Y. (2023). Research on the Innovation of Marketing Models in Chinese Commercial Banks in the Big Data Era. In Proceedings of the 5th International Conference on Fintech and Banking Innovation, 123-135. Singapore: Springer.

[4]. Chen, G. M., & Sun, X. L., (2023). Application research of XGBoost fusion model in bank customer churn prediction. Computer Knowledge and Technology, 13, 55–57.

[5]. Ji, C. Y., (2018) Research on imbalanced data classification and its application in bank marketing. [Journal Name in Chinese], 5, 55–57.

[6]. Sun, S. Q., (2023) 2023 China Banking Marketing Digitalization Industry Research Report. TMT Financial Group, iResearch Inc.

[7]. Fang, L. Z., & Cao, X. Y. (2023) Research on Intelligent Data Cleaning and Preprocessing Algorithms in University Informatization Systems. In Proceedings of the 2023 International Conference on Educational Data Mining, 45-58, Tokyo, Japan: IEEE.

[8]. Song, K., (2022) Research on Bank Marketing Data Analysis and Application Based on Mixed Sampling and Ensemble Learning. Master’s thesis, Guizhou University.

[9]. L. Yang, P. Sun, B. Yuan, Q. Long, D. Xiao., (2023) Implementation of Campus Recruitment Data Analysis and Visualization System Using Python. In: Proceedings of the 2023 International Conference on Educational Technology and Computer Science (ETCS 2023). IEEE, 2023: 234-239. DOI: 10.1109/ETCS.2023.00045

[10]. Shao W., (2022) Credit Risk Assessment and Prediction in P2P Lending Platforms: A Decision Tree-Based Approach". In: Proceedings of the 2022 International Conference on Financial Technology and Risk Management (FTRM 2022). IEEE, 2022: 112-117. DOI: 10.1109/FTRM.2022.00027

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