FinPT: Financial Risk Prediction with Profile Tuning on Pretrained
  Foundation Models
Yuwei Yin Yazheng Yang Jian Yang Qi Liu
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algorithms used are somewhat outdated, especially in the context of the fast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. In FinPT, we
fill the financial tabular data into the pre-defined instruction template,
obtain natural-language customer profiles by prompting LLMs, and fine-tune
large foundation models with the profile text to make predictions. We
demonstrate the effectiveness of the proposed FinPT by experimenting with a
range of representative strong baselines on FinBench. The analytical studies
further deepen the understanding of LLMs for financial risk prediction.