@inproceedings{chua-etal-2025-banking,
    title = "Banking Done Right: Redefining Retail Banking with Language-Centric {AI}",
    author = "Chua, Xin Jie  and
      Tan, Jeraelyn Ming Li  and
      Tan, Jia Xuan  and
      Poh, Soon Chang  and
      Goh, Yi Xian  and
      Choong, Debbie Hui Tian  and
      Mun, Foong Chee  and
      Yang, Sze Jue  and
      Chan, Chee Seng",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.45/",
    pages = "646--658",
    ISBN = "979-8-89176-333-3",
    abstract = "This paper presents Ryt AI, an LLM-native agentic framework that powers Ryt Bank to enable customers to execute core financial transactions through natural language conversation. This represents the first global regulator-approved deployment worldwide where conversational AI functions as the primary banking interface, in contrast to prior assistants that have been limited to advisory or support roles. Built entirely in-house, Ryt AI is powered by ILMU, a closed-source LLM developed internally, and replaces rigid multi-screen workflows with a single dialogue orchestrated by four LLM-powered agents (Guardrails, Intent, Payment, and FAQ). Each agent attaches a task-specific LoRA adapter to ILMU, which is hosted within the bank{'}s infrastructure to ensure consistent behavior with minimal overhead. Deterministic guardrails, human-in-the-loop confirmation, and a stateless audit architecture provide defense-in-depth for security and compliance. The result is Banking Done Right: demonstrating that regulator-approved natural-language interfaces can reliably support core financial operations under strict governance."
}