In the rapidly evolving world of Artificial Intelligence, Large Language Models (LLMs) are making waves, especially in specialized fields like finance. But how can we be sure these "financial whizzes" are truly intelligent and not just parroting information they've already seen? This is the critical question of data contamination, where models may have been exposed to test data, giving a false impression of their abilities. A new research paper introduces "CAP," a clever method for detecting this contamination. CAP, short for Consistency Amplification-based Data Contamination Detection, leverages a model's consistency in handling different versions of the same data. Think of it as a truth test for AI. By slightly altering the phrasing or order of information while keeping the core meaning intact, researchers can measure how consistently the model performs. A drop in consistency between a model's responses to the training data and test data can indicate contamination. This research applied CAP to several leading financial LLMs and popular benchmarks like FinEval, FinQA, and AlphaFin. The findings are eye-opening: some models showed clear signs of contamination, especially those trained on datasets compiled from public sources like textbooks or older benchmarks. This highlights the danger of unintentional contamination and the need for more rigorous validation. The study also revealed how contamination can spread: domain-specific models fine-tuned on a contaminated general-purpose model inherit the problem. This emphasizes the need for clean, carefully vetted data at every stage of model development. Ultimately, this research is a call for greater transparency and rigor in the field of financial AI. It underscores the importance of robust contamination detection methods like CAP to ensure that these powerful tools are truly learning and not just memorizing. Only then can we trust these models to handle the complexities of the financial world.