AI-assisted service channels in banking must decide not only customer intent but also when to transfer a conversation to a human specialist. Late escalation can increase customer frustration, repeat contact, and operational risk, especially when problems unfold gradually across multiple turns. This paper presents an interpretable escalation-risk workflow that combines turn-level sentence embeddings, conversation-state features, and lightweight sequence modeling. The evaluation uses publicly accessible banking intent data, complaint narratives, support-query text, and synthetic multi-turn conversation templates derived from those sources. Across both a binary escalation task and a three-level urgency task, the proposed workflow improves macro-F1 from 78.4 to 86.7 and reduces missed high-risk conversations by 23% relative to a rules-first baseline. The findings show that conversation- state modeling can materially improve escalation decisions in practical banking support operations.
Keywords
conversational AI, escalation detection, banking operations, customer support analytics, language models
Conclusion
This paper introduced a deployable and interpretable framework for escalation-risk detection in banking conversations. By combining sentence-level semantics with conversation-state dynam- ics, the approach delivers earlier and more reliable handoff signals than rules-first triage. For institutions expanding conversational service channels, the operational value is immediate: bet- ter protection for high-risk customers, faster resolution, and more effective queue management without depending on a large general-purpose assistant.
References
Bitext. (2023). Banking customer service intent dataset. https://github.com/bitext/ customer-support-intent-dataset.
Consumer Financial Protection Bureau. (2024). Consumer complaint database. https://www. consumerfinance.gov/data-research/consumer-complaints/.
Gans, N., Koole, G., and Mandelbaum, A. (2003). Telephone call centers: Tutorial, review, and research prospects. Manufacturing & Service Operations Management, 5(2), 79–141.
Larson, S., Mahendran, A., Peper, J. J., et al. (2019). An evaluation dataset for intent classifica- tion and out-of-scope prediction. Proceedings of EMNLP-IJCNLP, 1311–1316.
Reimers, N. and Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proceedings of EMNLP-IJCNLP, 3982–3992.
Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2019). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. NeurIPS EMC2 Workshop.
How to Cite This Paper
Viswatej Seela (2025). Detecting Escalation Risk in AI-Assisted Banking Conversations Using Conversation-State Signals. International Journal of Computer Techniques, 12(4). ISSN: 2394-2231.
