Augmenting XAI with LLMs: A Case Study in Banking Marketing Recommendation

Integrating AI-driven recommender systems has proven highly successful in many industries, prompting the banking sector to explore personalised client recommendations. Given the interpersonal nature of banking sales to corporate clients, wherein AI systems recommend to Relationship Managers who facilitate interactions with clients, there is a critical need for explainability in the AI-generated recommendations to support commercial activities. Our work leverages Generative AI and Large Language Models to synthesise natural language explanations for AI algorithms’ motivations, tailored for non-technical users in the banking environment. Through a case study in a major bank, Intesa Sanpaolo, our approach successfully replaces manual expert labour, offering scalable, efficient, and business-relevant explanations. Our study addresses key research questions and contributes by presenting an enriched presentation of SHAP explainer outputs in banking, validated against expert standards. We also explore the impact on the business, providing insights into the value of transparent AI-driven recommendations in the evolving landscape of banking services.