The text discusses how large language models (LLMs) can be leveraged to transform natural language into functional Python code for FinTech applications, thereby lowering technical barriers and accelerating innovation in the industry. It explains the process of creating a synthetic Text-to-Python dataset for FinTech using Gretel Navigator SDK's Data Designer mode. The dataset is carefully crafted with domain-specific terminology and scenarios to enable LLMs to generate precise, actionable code. The text also provides details on how to set up the workflow, build the dataset configuration, run the pipeline, and validate data quality. Finally, it introduces Gretel's Synthetic Text-to-Python Dataset for FinTech, a publicly available collection of 25,000 records tailored to support various FinTech coding applications.