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

ArXiv ID: 2308.00065 “View on arXiv”

Authors: Unknown

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in this field is lagging behind in recent years by the following two facts: 1) the algorithms used are somewhat outdated, especially in the context of the fast advance of generative AI and large language models (LLMs); 2) the lack of a unified and open-sourced financial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

Keywords: Financial Risk Prediction, Large Language Models, Profile Tuning, Generative AI, Benchmarking, Risk Management

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper focuses on applying existing LLM techniques (Profile Tuning) to financial tabular data, with limited novel mathematics, but it demonstrates high empirical rigor through a curated benchmark (FinBench), extensive experiments against strong baselines (XGBoost, etc.), and publicly released code and datasets.

  flowchart TD
    A["Research Goal:<br>Modernize Financial Risk Prediction<br>using LLMs & Open Benchmark"] --> B["Develop New Methodology:<br>FinPT - Profile Tuning"]
    B --> C["Create New Dataset:<br>FinBench - Unified Open Benchmark"]
    C --> D["Data Transformation:<br>Convert Tabular Data<br>to Natural Language Profiles"]
    D --> E["Computational Process:<br>Fine-Tune Large Foundation Models<br>with Profile Text"]
    E --> F["Key Findings:<br>Demonstrated Effectiveness vs Baselines<br>Deepened LLM Understanding"]
    F --> G["Outcome:<br>Modernized Approach for<br>Financial Risk Management"]