Hierarchical Reason-of-Contact Detection in Retail Banking Customer Interactions via LLM-Driven Taxonomy Induction

Yoages Kumar Mantri, Yagneswaran Sriraja, John Hartley, Greig A Cowan, Raad Khraishi, Abhinav Gupta

Keywords: Reason-of-Call Detection, Intent Classification, LLM Induced Hierarchical Taxonomy, FAISS Retrieval, Banking Conversations, Cost-Efficient, Dynamic Taxonomy Maintenance

TL;DR: We present a hierarchical, cost-efficient system for Reason-of-Call detection in retail banking conversations by combining unsupervised clustering, LLM-driven taxonomy induction, and fast vector-based retrieval.

Abstract: Retail banks handle high volumes of customer interactions across different channels that span various topics. Early and accurate detection of the intent of the customer is critical towards streamlining contact-center operations through efficient routing and handling of conversations. Mining of customer interactions leads to identification of friction points in customer journeys and offers valuable insights about customer needs. Existing approaches to define customer intents or contact reasons remain fragmented, manually maintained across organizations and relying on knowledge of specific business processes. We propose a framework that develops a dynamic hierarchical Reason-of-Contact (RoC) taxonomy to cover customer topics across hundreds of business processes. We further demonstrate the implementation of this taxonomy to a robust solution that identifies intents for all customer conversations across different channels. Our deployed system supports real time use with a 150 to 300 ms turnaround per conversation. It achieves up to 10\% improvement in F1 score over baseline approaches on a reference dataset. We also detail deployment considerations, including dynamic taxonomy updates, out-of-domain detection, and auditability. Finally, we present ablations and error analyses to characterize effectiveness.

Submission Type: Deployed

Submission Number: 379