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

TL;DR

This paper introduces FinPT, a novel method that fine-tunes large pretrained models using customer profiles generated by LLMs, and presents FinBench, a new open dataset for financial risk prediction, improving over outdated algorithms.

Contribution

The paper proposes a new profile tuning approach with large foundation models and introduces FinBench, a comprehensive financial risk dataset, addressing gaps in current research and benchmarks.

Findings

FinPT outperforms strong baselines on FinBench datasets.

Using customer profiles enhances risk prediction accuracy.

The approach deepens understanding of LLMs in financial contexts.

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in this field is lagging behind in recent years by the following two facts: 1) the algorithms used are somewhat outdated, especially in the context of the fast advance of generative AI and large language models (LLMs); 2) the lack of a unified and open-sourced financial benchmark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.