<![CDATA[Micro, small, and medium enterprises (MSMEs) are the backbone of Indonesia's economy, yet many owners face persistent gaps in financial literacy and difficulty navigating complex financial and tax regulations. General-purpose large language models (LLMs) can make such knowledge more accessible, but they often produce fluent yet unsourced or hallucinated answers, which is unacceptable in a financial and regulatory context. This study designs and evaluates IndoRAG-FA, a hybrid Retrieval-Augmented Generation (RAG) framework with explicit hallucination mitigation for Bahasa Indonesia MSME financial advisory. The framework integrates coherence-based semantic chunking; hybrid retrieval combining lexical BM25 and dense multilingual embeddings fused through reciprocal rank fusion; cross-encoder re-ranking; and grounded generation that produces source-cited answers with a confidence-based abstention mechanism and a groundedness verifier. A curated knowledge base was built from authoritative Indonesian financial and tax sources, and an expert-validated benchmark of representative MSME questions, including out-of-scope items, was developed. The framework was compared with an LLM-only baseline and a vanilla RAG baseline using retrieval metrics (Recall@k, MRR, nDCG), faithfulness, answer relevance and correctness, hallucination rate, abstention precision and recall, and a human-expert rubric. Findings indicate that a retrieval-grounded, abstention-aware design offers a practical and trustworthy path toward deployable financial advisory for Indonesian MSMEs and provides a reusable evaluation protocol for domain-specific LLM systems.]]>
Copyrights © 2026
