Fin_Agent reports multilingual financial NLP results at FinEval 2026

Key points

• Greek financial NER reached 0.9521 public F1 and 0.8884 private F1, using numeric rules with zero-shot Qwen2.5-32B classification.
• Japanese implicit communication recognition hit 98.0% test accuracy with an interpretable cue-based ordinal scorer.
• Multilingual financial QA scored 0.3023 and 0.3419 ROUGE-1 F1 on public and private splits, using BGE-M3 retrieval with Qwen3-32B.

Why it matters for marketers: Teams building multilingual finance text tools get benchmark baselines and linked code, but the authors say their rules are dataset-specific and need validation before reuse.