Recently, large models have sparked a热潮 in the financial system, with banks, insurance companies, securities firms, and other institutions rapidly deploying these models. However, experts point out that the application of large models in the financial sector still faces obstacles such as immature technology, compliance restrictions, and insufficient scenarios. Specifically, the current large model technology itself has issues such as low accuracy and limited judgment capabilities, which do not meet the high accuracy requirements of the financial industry. Additionally, the training and use of large models must comply with data security regulations, subjecting them to compliance constraints. Furthermore, the promotion of large models requires a rich array of application scenarios, which are currently insufficient in the financial sector. Industry insiders indicate that the application of large models in finance is currently limited to superficial areas such as intelligent customer service, and it is still far from being applied to core business operations. It remains necessary to continue breaking through technological, compliance, and scenario challenges in order to truly harness the potential of large models in the financial field.