This paper focuses on the application and optimization of LSTM model in
financial risk prediction. The study starts with an overview of the
architecture and algorithm foundation of LSTM, and then details the model
training process and hyperparameter tuning strategy, and adjusts network
parameters through experiments to improve performance. Comparative experiments
show that the optimized LSTM model shows significant advantages in AUC index
compared with random forest, BP neural network and XGBoost, which verifies its
efficiency and practicability in the field of financial risk prediction,
especially its ability to deal with complex time series data, which lays a
solid foundation for the application of the model in the actual production
environment.

该研究侧重于 LSTM 模型在金融风险预测中的应用和优化，实验结果表明，经过优化的 LSTM 模型在 AUC 指数上相比随机森林、BP 神经网络和 XGBoost 都有显著优势，验证了其在金融风险预测领域的有效性和实用性，尤其是处理复杂时间序列数据的能力，为该模型在实际生产环境中的应用奠定了坚实基础。

通过优化 LSTM 模型性能和比较分析推进金融风险预测

Advancing Financial Risk Prediction Through Optimized LSTM Model  Performance and Comparative Analysis

The integration of Artificial Intelligence (AI) techniques, particularly
large language models (LLMs), in finance has garnered increasing academic
attention. Despite progress, existing studies predominantly focus on tasks like
financial text summarization, question-answering (Q$\&$A), and stock movement
prediction (binary classification), with a notable gap in the application of
LLMs for financial risk prediction. Addressing this gap, in this paper, we
introduce \textbf{RiskLabs}, a novel framework that leverages LLMs to analyze
and predict financial risks. RiskLabs uniquely combines different types of
financial data, including textual and vocal information from Earnings
Conference Calls (ECCs), market-related time series data, and contextual news
data surrounding ECC release dates. Our approach involves a multi-stage
process: initially extracting and analyzing ECC data using LLMs, followed by
gathering and processing time-series data before the ECC dates to model and
understand risk over different timeframes. Using multimodal fusion techniques,
RiskLabs amalgamates these varied data features for comprehensive multi-task
financial risk prediction. Empirical experiment results demonstrate RiskLab's
effectiveness in forecasting both volatility and variance in financial markets.
Through comparative experiments, we demonstrate how different data sources
contribute to financial risk assessment and discuss the critical role of LLMs
in this context. Our findings not only contribute to the AI in finance
application but also open new avenues for applying LLMs in financial risk
assessment.

用人工智能在金融领域应用的一项新型框架 RiskLabs，通过整合各种金融数据和大语言模型来分析和预测金融风险。通过实证实验结果，我们展示了 RiskLabs 在金融市场的波动性和方差预测中的有效性，并讨论了大语言模型在这一领域中的关键作用。这些发现不仅对 AI 在金融领域的应用做出了贡献，也为在金融风险评估中应用大语言模型开启了新的途径。

RiskLabs：基于多源数据的大型语言模型预测金融风险

RiskLabs: Predicting Financial Risk Using Large Language Model Based on  Multi-Sources Data

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.

金融风险预测在金融领域中起着关键作用。机器学习方法已被广泛应用于自动检测潜在风险，从而节省劳动成本。然而，近年来在这一领域的发展在以下两个方面滞后：1）所使用的算法有些过时，特别是在生成式人工智能和大型语言模型（LLM）快速发展的背景下；2）缺乏统一的、开源的金融基准已经阻碍了相关研究多年。为了解决这些问题，我们提出了 FinPT 和 FinBench：前者是一种新颖的金融风险预测方法，它在大型预训练基础模型上进行 Profile Tuning；后者是一组关于金融风险的高质量数据集，如违约、欺诈和流失。在 FinPT 中，我们将金融表格数据填充到预定义的指令模板中，通过提示 LLMs 获取自然语言客户配置文件，并使用配置文件文本对大型基础模型进行微调以进行预测。通过在 FinBench 上与一系列代表性的强基线进行实验，我们证明了所提出的 FinPT 的有效性。分析研究进一步深化了对于金融风险预测的大型语言模型的理解。