We present Fin_Agent, our submission to the FinEval 2026 shared task on multilingual financial language understanding. Our systems address Greek financial named-entity recognition, Japanese implicit communication recognition, and multilingual financial question answering. For Greek NER, we combine annotation-aware numeric rules with zero-shot Qwen2.5-32B classification, achieving 0.9521 public F1 and 0.8884 private F1. For Japanese implicit communication, an interpretable cue-based ordinal scorer achieves 98.0% test accuracy. For multilingual financial QA, BGE-M3 retrieval with Qwen3-32B generation achieves 0.3023 and 0.3419 ROUGE-1 F1 on the public and private splits. Key limitations include dataset-specific rules, handcrafted cues, and limited supervision. Implementation code is available https://github.com/Rashidnira/finagent-fineval-2026.