Generative AI Enhanced Financial Risk Management Information Retrieval

• URL: http://arxiv.org/abs/2504.06293v2
• Date: Thu, 10 Apr 2025 03:08:59 GMT
• Title: Generative AI Enhanced Financial Risk Management Information Retrieval
• Authors: Amin Haeri, Jonathan Vitrano, Mahdi Ghelichi,
• Abstract summary: RiskData is a dataset curated for finetuning embedding models in risk management.<n>RiskEmbed is a finetuned embedding model designed to improve retrieval accuracy in financial question-answering systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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