Natural language processing in bank marketing: a systematic review and strategic gap analysis

Purpose Natural language processing (NLP), a subfield of artificial intelligence, allows organizations to gain insights from unstructured text, such as e-mails, documents and social media posts. The automated interpretation of human language holds considerable potential to sharpen marketing strategies, deepen customer engagement and unlock new value. Yet academic knowledge on NLP in the context of bank marketing remains scattered. This paper consolidates that knowledge and identifies research gaps, with particular attention to where NLP can be integrated into the customer journey and operational excellence. Design/methodology/approach Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol, we screened peer-reviewed journal articles published between 2014 and 2024 (n = 109). We then conducted a structured review of analytical marketing in banking and NLP applications in general marketing. Finally, we used sentence-transformer embeddings and uniform manifold approximation and projection to visualize the thematic landscape and identify under-explored areas. Findings Only 8 papers study NLP within bank marketing; 74 additional papers examine NLP in marketing more generally, and a further 27 explore broader marketing applications in banking. The existing literature concentrates primarily on customer retention, whereas other areas, including customer acquisition, personalized engagement and the use of external text data, receive comparatively little attention. Originality/value This study provides one of the first systematic reviews focused specifically on NLP in bank marketing. By aligning prior research with the customer journey and the marketing mix, it offers a structured reference for researchers and practitioners interested in applying NLP for growth, improved customer experience and innovation in the banking sector.