Foong Chee Mun


2025

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Banking Done Right: Redefining Retail Banking with Language-Centric AI
Xin Jie Chua | Jeraelyn Ming Li Tan | Jia Xuan Tan | Soon Chang Poh | Yi Xian Goh | Debbie Hui Tian Choong | Foong Chee Mun | Sze Jue Yang | Chee Seng Chan
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track

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.

2024

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MalayMMLU: A Multitask Benchmark for the Low-Resource Malay Language
Soon Chang Poh | Sze Jue Yang | Jeraelyn Ming Li Tan | Lawrence Leroy Tze Yao Chieng | Jia Xuan Tan | Zhenyu Yu | Foong Chee Mun | Chee Seng Chan
Findings of the Association for Computational Linguistics: EMNLP 2024