From Clicks to Intent: Cross-Platform Session Embeddings with LLM-Distilled Taxonomy for Financial Services Recommendations
arXiv:2606.26277v1 Announce Type: cross Abstract: Sequential user behavior modeling is widely adopted in industrial recommender systems; however, significant gaps remain in financial services, where pre-login web interactions and authenticated in-app experiences differ drastically. Specifically, pre-login web users typically explore new products, whereas logged-in app users focus on account servicing. Due to the challenge of cross-channel entity resolution (e.g., matching anonymous web sessions to authenticated mobile accounts), web-based intent signals remain underutilized for post-authentica
The increasing sophistication of LLMs and the critical need for financial institutions to unify customer data across disparate channels drives this innovation now.
This development could significantly enhance personalized financial recommendations, leading to higher customer engagement and improved product adoption for financial services.
Financial institutions can now better leverage pre-login web behavior for personalized in-app experiences, bridging a significant gap in cross-platform user understanding.
- · Financial Services
- · AI/ML providers
- · Consumer Banking
- · Traditional recommendation systems
- · Marketing agencies (less need for manual segmentation)
Financial institutions gain a more holistic view of customer intent, enabling hyper-personalized product offerings.
Increased customer satisfaction and loyalty in financial services due to more relevant interactions, potentially increasing switching costs.
Consolidation in financial services as institutions with advanced AI capabilities gain a significant competitive edge in customer acquisition and retention.
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Read at arXiv cs.LG