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.
@article{arxiv.2510.07645,
title = {Banking Done Right: Redefining Retail Banking with Language-Centric AI},
author = {Xin Jie Chua and Jeraelyn Ming Li Tan and Jia Xuan Tan and Soon Chang Poh and Yi Xian Goh and Debbie Hui Tian Choong and Chee Mun Foong and Sze Jue Yang and Chee Seng Chan},
journal= {arXiv preprint arXiv:2510.07645},
year = {2025}
}