FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models
FinPT uses Profile Tuning on large language models for financial risk prediction, evaluated on the FinBench dataset.
What it examines
The paper introduces FinPT, a novel method for financial risk prediction using Profile Tuning on large pretrained foundation models, and FinBench, a benchmark of high-quality financial risk datasets. It aims to address outdated algorithms and the lack of a unified financial benchmark.
What it concludes
The research suggests that FinPT can significantly enhance financial risk prediction, with potential applications in default, fraud, and churn prediction. Future research could explore other tuning strategies and expand the benchmark. The study highlights the importance of unified benchmarks and advanced models in financial risk prediction.
Evidence objects
The research suggests that FinPT can significantly enhance financial risk prediction, with potential applications in default, fraud, and churn prediction. Future research could explore other tuning strategies and expand the benchmark. The study highlights the importance of unified benchmarks and advanced models in financial risk prediction.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: 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in this field is lagging behind in recent years by the following two facts: 1) the algorithms used are somewhat outdated, especially in the context of the fast advance of gen… ▽ More Financial risk prediction plays a crucial role in the financial sector.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in this field is lagging behind in recent years by the following two facts: 1) the algorithms used are somewhat outdated, especially in the context of the fast advance of generative AI and large language models (LLMs); 2) the lack of a unified and open-sourced financial benchmark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. △ Less
Source row: 857 · abstract type: unknown