FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models

Understand

Financial risk prediction plays a crucial role in the financial sector.

• Machine learning methods have been widely applied for automatically detecting potential risks and thus saving the cost of labor.
• However, the development in this field is lagging behind in recent years by the following two facts: 1) the algorithms used are somewhat outdated, especially in the context of the fast advance of generative AI and large language models (LLMs); 2) the lack of a unified and open-sourced financial benchmark has impeded the related research for years.
• To tackle these issues, we propose FinPT and FinBench: the former is a novel approach for financial risk prediction that conduct Profile Tuning on large pretrained foundation models, and the latter is a set of high-quality datasets on financial risks such as default, fraud, and churn.