用金融监管文档训练模型，提升风险问答系统检索精度。
Generative AI Enhanced Financial Risk Management Information Retrieval
- 基于OSFI 1991–2024年94份指南构建专用数据集
- 微调后的RiskEmbed在排名指标上显著优于通用模型
- 适合金融机构和研究者开发精准的AI风控工具
金融风险管理涉及识别、评估与应对风险以维持稳定并满足合规要求。从大量监管文件中提取相关洞察是一项复杂挑战，需依赖先进的检索与语言模型。本文提出RiskData数据集，专为金融风险领域微调嵌入模型而设计；并构建RiskEmbed模型，旨在提升金融问答系统中的检索准确性。该数据集源自加拿大金融机构监管办公室（OSFI）1991至2024年间发布的94份监管指南。我们对前沿的Sentence BERT嵌入模型进行微调，以增强其在检索增强生成（RAG）系统中的领域适应能力。实验表明，RiskEmbed在排名性能上显著优于通用及金融领域嵌入模型，带来实质性提升。通过开源数据集与模型，本工作为金融机构与研究者提供可复用资源，助力更精准高效的金融风险智能管理解决方案。
原文摘要 · Abstract (English)
Risk management in finance involves recognizing, evaluating, and addressing financial risks to maintain stability and ensure regulatory compliance. Extracting relevant insights from extensive regulatory documents is a complex challenge requiring advanced retrieval and language models. This paper introduces RiskData, a dataset specifically curated for finetuning embedding models in risk management, and RiskEmbed, a finetuned embedding model designed to improve retrieval accuracy in financial question-answering systems. The dataset is derived from 94 regulatory guidelines published by the Office of the Superintendent of Financial Institutions (OSFI) from 1991 to 2024. We finetune a state-of-the-art sentence BERT embedding model to enhance domain-specific retrieval performance typically for Retrieval-Augmented Generation (RAG) systems. Experimental results demonstrate that RiskEmbed significantly outperforms general-purpose and financial embedding models, achieving substantial improvements in ranking metrics. By open-sourcing both the dataset and the model, we provide a valuable resource for financial institutions and researchers aiming to develop more accurate and efficient risk management AI solutions.
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