Retrieval-augmented generation (RAG) is increasingly used in financial applications where generated answers must be grounded in specific documents, filings, or disclosures. Yet even with relevant context, answers may contradict, overextend, or misinterpret the supplied evidence. This paper presents a comparative study of intrinsic hallucination detection for financial RAG, where the task is to determine whether an answer is faithful to a supplied question-context pair. We evaluate DSPy-based prompt optimization, fine-tuned detectors, encoder-based detectors, and LLM-as-Judge frameworks across public benchmarks and finance-specific settings. To examine finance-specific behavior, we construct FinDER-HD, a balanced hallucination-detection benchmark derived from FinDER by synthesizing unfaithful counterparts for faithful financial question-context-answer examples. We further evaluate filing-scale robustness on PHANTOM, which varies context length, evidence position, and filing type. Results show that detector performance varies substantially across datasets, financial topics, reasoning types, and context regimes. DSPy-based prompt optimization is competitive under limited supervision, but robustness differs markedly by optimization strategy: demonstration-heavy methods perform strongly in short contexts yet degrade more sharply as context grows, whereas COPRO and especially GEPA remain substantially more stable. Selected LLM-as-Judge methods also perform strongly in finance-specific settings, but their operating behavior depends on threshold choice and prediction bias. These findings motivate deployment-oriented evaluation of financial RAG guardrails beyond aggregate F1, including finance-specific robustness, filing-scale stress testing, prediction bias, threshold calibration, and contextwindow feasibility.