Imagine trying to predict the future of a company's financial health. It's a complex puzzle, and large language models (LLMs) might seem like the perfect tool for the job. After all, they can analyze mountains of text, potentially uncovering hidden insights within financial reports. But a new research paper reveals a surprising twist: traditional methods are still outperforming LLMs in forecasting credit ratings. The study found that while LLMs excel at processing textual information from sources like SEC filings, they struggle to effectively integrate numerical data, such as financial and macroeconomic indicators. This weakness becomes apparent when comparing LLMs to a more established method like XGBoost, which seamlessly combines textual insights with numerical data, demonstrating greater accuracy in predicting credit rating changes. This isn’t to say LLMs are useless in finance. When analyzing text alone, they can pick up signals traditional methods miss. This hints at the possibility of powerful future combinations of both techniques. However, the research highlights a critical limitation of current LLMs: they don’t reason like human analysts. Humans naturally synthesize textual and numerical data to build a holistic picture, and that's where traditional methods still have the edge. This advantage is further amplified by the interpretability of traditional models, making them easier to understand and trust in the heavily regulated financial world. This study underscores the need for continued research into how LLMs can more effectively process multimodal data. Future models may overcome these limitations, but for now, traditional methods remain the gold standard for credit rating forecasting, offering a powerful combination of accuracy and explainability.