FinTech software, crucial for both safety and timely market deployment, presents a compelling case for automated acceptance testing against regulatory business rules. However, the inherent challenges of comprehending unstructured natural language descriptions of these rules and crafting comprehensive test cases demand human intelligence. The emergence of Large Language Models (LLMs) holds promise for automated test case generation, leveraging their natural language processing capabilities. Yet, their dependence on human intervention for effective prompting hampers efficiency.

In response, we introduce a groundbreaking, fully automated approach for generating high-coverage test cases from natural language business rules. Our methodology seamlessly integrates the versatility of LLMs with the predictability of algorithmic methods. We fine-tune pre-trained LLMs for improved information extraction accuracy and algorithmically generate comprehensive testable scenarios for the extracted business rules. Our prototype, LLM4Fin, is designed for testing real-world stock-trading software. Experimental results demonstrate LLM4Fin's superiority over both state-of-the-art LLM, such as ChatGPT, and skilled testing engineers. We achieve remarkable performance, with up to 98.18% and an average of $20%-110%$ improvement on business scenario coverage, and up to 93.72% on code coverage, while reducing the time cost from 20 minutes to a mere 7 seconds. These results provide robust evidence of the framework's practical applicability and efficiency, marking a significant advancement in FinTech software testing.