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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.16369 |
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| _version_ | 1866916958864146432 |
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| author | Srinivasan, Akshay Govind George, Ryan Jacob Joe, Jayden Koshy Kant, Hrushikesh R, Harshith M Sundar, Sachin Suresh, Sudharshan Vimalkanth, Rahul Vijayavallabh |
| author_facet | Srinivasan, Akshay Govind George, Ryan Jacob Joe, Jayden Koshy Kant, Hrushikesh R, Harshith M Sundar, Sachin Suresh, Sudharshan Vimalkanth, Rahul Vijayavallabh |
| contents | Accurate and reliable knowledge retrieval is vital for financial question-answering, where continually updated data sources and complex, high-stakes contexts demand precision. Traditional retrieval systems rely on a single database and retriever, but financial applications require more sophisticated approaches to handle intricate regulatory filings, market analyses, and extensive multi-year reports. We introduce a framework for financial Retrieval Augmented Generation (RAG) that leverages agentic AI and the Multi-HyDE system, an approach that generates multiple, nonequivalent queries to boost the effectiveness and coverage of retrieval from large, structured financial corpora. Our pipeline is optimized for token efficiency and multi-step financial reasoning, and we demonstrate that their combination improves accuracy by 11.2% and reduces hallucinations by 15%. Our method is evaluated on standard financial QA benchmarks, showing that integrating domain-specific retrieval mechanisms such as Multi-HyDE with robust toolsets, including keyword and table-based retrieval, significantly enhances both the accuracy and reliability of answers. This research not only delivers a modular, adaptable retrieval framework for finance but also highlights the importance of structured agent workflows and multi-perspective retrieval for trustworthy deployment of AI in high-stakes financial applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16369 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Financial RAG with Agentic AI and Multi-HyDE: A Novel Approach to Knowledge Retrieval and Hallucination Reduction Srinivasan, Akshay Govind George, Ryan Jacob Joe, Jayden Koshy Kant, Hrushikesh R, Harshith M Sundar, Sachin Suresh, Sudharshan Vimalkanth, Rahul Vijayavallabh Information Retrieval Artificial Intelligence Computational Engineering, Finance, and Science H.4; H.5; H.3.3 Accurate and reliable knowledge retrieval is vital for financial question-answering, where continually updated data sources and complex, high-stakes contexts demand precision. Traditional retrieval systems rely on a single database and retriever, but financial applications require more sophisticated approaches to handle intricate regulatory filings, market analyses, and extensive multi-year reports. We introduce a framework for financial Retrieval Augmented Generation (RAG) that leverages agentic AI and the Multi-HyDE system, an approach that generates multiple, nonequivalent queries to boost the effectiveness and coverage of retrieval from large, structured financial corpora. Our pipeline is optimized for token efficiency and multi-step financial reasoning, and we demonstrate that their combination improves accuracy by 11.2% and reduces hallucinations by 15%. Our method is evaluated on standard financial QA benchmarks, showing that integrating domain-specific retrieval mechanisms such as Multi-HyDE with robust toolsets, including keyword and table-based retrieval, significantly enhances both the accuracy and reliability of answers. This research not only delivers a modular, adaptable retrieval framework for finance but also highlights the importance of structured agent workflows and multi-perspective retrieval for trustworthy deployment of AI in high-stakes financial applications. |
| title | Enhancing Financial RAG with Agentic AI and Multi-HyDE: A Novel Approach to Knowledge Retrieval and Hallucination Reduction |
| topic | Information Retrieval Artificial Intelligence Computational Engineering, Finance, and Science H.4; H.5; H.3.3 |
| url | https://arxiv.org/abs/2509.16369 |