In the field of financial technology, a major breakthrough is rewriting industry rules. The SUFE-AIFLM-Lab team led by Professor Zhang Liwen of the School of Statistics and Data Science of Shanghai University of Finance and Economics has officially opened source for the Fin-R1 model jointly developed by Caiyuexingchen. This innovative achievement has caused an uproar in the industry. This financial-specific big model based on Qwen2.5-7B architecture has demonstrated excellent performance in multiple financial benchmarks through advanced reinforcement learning and training methods, becoming a new benchmark in the industry.
The most striking feature of the Fin-R1 model is its amazing efficiency. Although it only has a 7B parameter scale, its performance surpasses many competitors with larger parameter scales. Especially in key tasks such as Financial Form Reasoning (FinQA) and Conversational Financial QA (ConvFinQA), Fin-R1 won the championship with an absolute advantage, fully demonstrating its in-depth understanding and accurate analysis in the financial field.
The design of this model fully takes into account the needs of the core financial business scenarios, and its functional coverage is amazing. From financial code writing, pricing model construction to risk assessment script development, from accurate quantitative analysis to complex report calculation, Fin-R1 can be completed with ease. In addition, it can smoothly generate English financial models and professional reports, provide financial security compliance analysis, realize intelligent risk control functions such as transaction anti-fraud and default prediction, and even conduct ESG sustainability analysis such as environment, society, and governance. It can be called an all-round AI assistant in the financial field.
Fin-R1's success is inseparable from its advanced technical route. The R&D team built the model architecture based on Qwen2.5-7B-Instruct and innovatively used the DeepSeek-R1 framework for "data distillation" and "dual-wheel quality screening". Through the training method of combining supervision and fine-tuning (SFT) and reinforcement learning (RL) of high-quality thinking chain data, AI assistants in this financial field have been successfully created, providing strong technical support for the digital transformation of the financial industry.
It is worth mentioning that Fin-R1 not only supports the Chinese environment, but also can conduct financial modeling, report generation and dialogue interaction in the English environment, showing extremely strong cross-language capabilities. The advent of this open source model will provide strong support for the digital transformation of the financial industry and is expected to become a good assistant for financial analysts, risk control experts and investment consultants, pushing financial technology to new heights.