FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models (2308.00065v1)

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in this field is lagging behind in recent years by the following two facts: 1) the algorithms used are somewhat outdated, especially in the context of the fast advance of generative AI and LLMs; 2) the lack of a unified and open-sourced financial benchmark has impeded the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

• The paper demonstrates that profile tuning on pretrained foundation models significantly improves financial risk prediction across multiple datasets.
• It introduces FinBench, an open-source benchmark with diverse, high-quality financial datasets to evaluate risk models using both tabular data and profile text.
• Experimental results reveal that LLM-based profile tuning consistently outperforms traditional tree-based and neural network models in predicting defaults, fraud, and churn.

An Overview of "FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models"

The paper "FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models" introduces an innovative approach, termed FinPT, to enhance financial risk prediction by leveraging large pretrained foundation models. This work addresses two significant challenges in the domain of financial risk prediction: the outdated nature of existing algorithms in the face of rapid advancements in LLMs and the absence of a unified, open-source financial benchmark.

Key Contributions

1. Profile Tuning Approach: FinPT transforms tabular financial data into natural language customer profiles using LLMs like ChatGPT. This transformation enables the fine-tuning of foundation models with these textual profiles to predict financial risks, such as default, fraud, and churn.
2. FinBench Benchmark: The paper introduces FinBench, a comprehensive collection of high-quality financial datasets specifically designed to evaluate financial risk prediction. FinBench includes datasets covering common financial risks and facilitates the evaluation of machine learning models using both tabular data inputs and profile text inputs.
3. Experimental Evaluation: The researchers rigorously evaluate FinPT against established baselines, such as tree-based algorithms and neural networks designed for tabular data. They demonstrate FinPT's superiority across various datasets, indicating the potential of LLMs in improving financial risk prediction accuracy.

Experimental Insights

FinPT is deployed on a variety of foundation models, including BERT, GPT-2, and Flan-T5, revealing that models like Flan-T5 outperform others when fully fine-tuned. The results indicate that combining FinPT with state-of-the-art LLMs surpasses traditional and neural network baselines, highlighting the efficacy of profile tuning. Furthermore, they experimented with profile tuning across multiple datasets, observing significant improvements, particularly for datasets with initially low predictive scores.

The analysis also indicates that while tree-based models such as CatBoost and LightGBM remain robust competitors, neural networks tailored for tabular data often lag behind tree-based models and FinPT when predicting financial risks. This finding underscores the challenges neural networks face in handling tabular data and the potential of LLM-based transformations to bridge this gap.

Implications and Future Directions

The implications of this research are profound for both the academic community and the financial industry. By demonstrating that LLMs can effectively contextualize financial data and enhance predictive models, FinPT sets a precedent for employing advanced language processing techniques in finance. Practically, this means financial institutions can potentially implement more accurate risk assessment models, reducing manual oversight and enhancing efficiency.

From a theoretical perspective, this work opens new avenues for exploring the role of LLMs beyond traditional text processing tasks, thereby encouraging further research into cross-domain applications of these models. Future work could explore optimizing profile tuning strategies and scaling models to accommodate larger datasets and more complex financial scenarios. Additionally, integrating explainability and fairness considerations into such models could further enhance their applicability and acceptance in sensitive areas like financial services.

In conclusion, by introducing and validating a novel approach that leverages the advancements in LLMs for financial risk prediction, this paper makes a meaningful contribution to the intersection of artificial intelligence and finance, providing a solid foundation for future explorations in this dynamic field.