系统梳理AI与NLP在银行营销中的应用,揭示关键空白与机遇。
Leveraging AI and NLP for Bank Marketing: A Systematic Review and Gap Analysis
- 采用PRISMA方法系统综述文献,结合语义映射分析技术
- 发现银行营销中NLP研究严重不足,尤其在客户全旅程应用
- 为学术界和银行实践提供可落地的NLP创新框架
本文探讨人工智能与自然语言处理在银行营销中的日益增长影响,强调其在优化营销策略、提升客户参与度及创造价值方面的演变作用。尽管AI与NLP在通用营销领域已有广泛研究,但其在银行业的具体应用与潜力仍缺乏深入理解。本研究通过系统性综述与战略分析,填补这一空白,聚焦其在客户旅程与运营卓越中的整合应用。采用PRISMA方法系统回顾现有文献,评估当前发展态势,并结合句向量模型(Sentence Transformers)与UMAP进行语义映射,开展战略差距分析。结果显示,针对银行营销的NLP研究极为有限。分析识别出多个未充分探索领域,如客户获取、留存与个性化互动等以客户为中心的应用场景,为未来研究提供方向。本研究不仅绘制了当前技术应用图景,还为构建基于NLP的增长与创新框架提供了可操作洞察,凸显其在提升运营效率与合规性方面的作用,对改善客户体验、提升盈利能力与推动行业创新具有深远意义。
原文摘要 · Abstract (English)
This paper explores the growing impact of AI and NLP in bank marketing, highlighting their evolving roles in enhancing marketing strategies, improving customer engagement, and creating value within this sector. While AI and NLP have been widely studied in general marketing, there is a notable gap in understanding their specific applications and potential within the banking sector. This research addresses this specific gap by providing a systematic review and strategic analysis of AI and NLP applications in bank marketing, focusing on their integration across the customer journey and operational excellence. Employing the PRISMA methodology, this study systematically reviews existing literature to assess the current landscape of AI and NLP in bank marketing. Additionally, it incorporates semantic mapping using Sentence Transformers and UMAP for strategic gap analysis to identify underexplored areas and opportunities for future research. The systematic review reveals limited research specifically focused on NLP applications in bank marketing. The strategic gap analysis identifies key areas where NLP can further enhance marketing strategies, including customer-centric applications like acquisition, retention, and personalized engagement, offering valuable insights for both academic research and practical implementation. This research contributes to the field of bank marketing by mapping the current state of AI and NLP applications and identifying strategic gaps. The findings provide actionable insights for developing NLP-driven growth and innovation frameworks and highlight the role of NLP in improving operational efficiency and regulatory compliance. This work has broader implications for enhancing customer experience, profitability, and innovation in the banking industry.
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