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While AI-driven financial advisory has become increasingly essential for individuals to navigate volatile markets that offer complex financial products, limited data and privacy concerns pose challenges in its development. This research explores the potential of combining Retrieval-Augmented Generation (RAG) with synthetic data, which imitates real-world data without compromising privacy, in enhancing financial question-answering systems. We propose a framework to generate and incorporate synthetic data into the RAG model with strategies to enhance retrieval processes. The proposed framework is compared with a baseline model without synthetic data to evaluate our approach using the Retrieval-Augmented Generation Assessment (RAGAS). The result shows that the integration of synthetic data can improve recall, precision, and faithfulness of the generated responses. However, relevancy can degrade due to the broader scope of retrieved data. The outcome demonstrates that synthetic data can enhance the accessibility and accuracy of financial data while safeguarding privacy in financial question-answering systems.

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RAG-Driven Financial QA: Preserving Privacy and Enhancing Performance with Synthetic Data

  • 会议论文

  • 发表日期：

    2026年

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  • Niorn Suchonwanich,
  • Siranee Nuchitprasitchai,
  • Kanchana Viriyapant,
  • Sucha Smanchat

摘要

While AI-driven financial advisory has become increasingly essential for individuals to navigate volatile markets that offer complex financial products, limited data and privacy concerns pose challenges in its development. This research explores the potential of combining Retrieval-Augmented Generation (RAG) with synthetic data, which imitates real-world data without compromising privacy, in enhancing financial question-answering systems. We propose a framework to generate and incorporate synthetic data into the RAG model with strategies to enhance retrieval processes. The proposed framework is compared with a baseline model without synthetic data to evaluate our approach using the Retrieval-Augmented Generation Assessment (RAGAS). The result shows that the integration of synthetic data can improve recall, precision, and faithfulness of the generated responses. However, relevancy can degrade due to the broader scope of retrieved data. The outcome demonstrates that synthetic data can enhance the accessibility and accuracy of financial data while safeguarding privacy in financial question-answering systems.