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<p>The need for advanced financial advisory systems has grown as global markets demand precise, real-time insights. Traditional models often face limitations in adaptability, language diversity, and data reliability. This paper presents DeepFinLLM 2.0, a unified pipeline that orchestrates multilingual Natural Language Processing (NLP), DeepSeek R1-0528 retrieval, cross-validated multi-API data fusion, and Beetle Antenna Search (BAS) driven optimization into a real-time financial advisory system. DeepFinLLM 2.0 Utilizes an advanced multilingual pipeline catering to queries in over 100 languages while delivering proven robust performance in 12 key languages, making it versatile yet reliable for global financial advisory applications. The integration of DeepSeek R1-0528 improves retrieval efficiency, enhancing accuracy and reducing misinformation. Additionally, BAS dynamically optimizes system parameters and query processing, ensuring low latency even when handling multiple concurrent API calls across diverse languages. Moreover, the system Employs multiple financial data sources, reducing dependence on a single API and strengthening data integrity through cross-validation. These enhancements significantly improve performance, achieving 96.2% accuracy, an F1 score of 0.94, and an Exact Match rate of 92%. With an optimized 0.75-second response time, DeepFinLLM 2.0 provides timely financial insights, including risk assessments and portfolio recommendations. DeepFinLLM 2.0 demonstrates that integrating multilingual NLP, enhanced retrieval, multi-API orchestration, and dynamic BAS tuning within a single architecture can achieve state-of-the-art performance for real-time financial decision-making, offering a scalable solution for institutional and retail investors in dynamic markets.</p>

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DeepFinLLM 2.0: an optimized and scalable multilingual financial advisor unleashing strategic insights through multi-API orchestration and large language models

  • 期刊论文

  • 发表日期：

    2025年9月29日

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  • Veerababu Reddy,
  • N. Veeranjaneyulu

摘要

The need for advanced financial advisory systems has grown as global markets demand precise, real-time insights. Traditional models often face limitations in adaptability, language diversity, and data reliability. This paper presents DeepFinLLM 2.0, a unified pipeline that orchestrates multilingual Natural Language Processing (NLP), DeepSeek R1-0528 retrieval, cross-validated multi-API data fusion, and Beetle Antenna Search (BAS) driven optimization into a real-time financial advisory system. DeepFinLLM 2.0 Utilizes an advanced multilingual pipeline catering to queries in over 100 languages while delivering proven robust performance in 12 key languages, making it versatile yet reliable for global financial advisory applications. The integration of DeepSeek R1-0528 improves retrieval efficiency, enhancing accuracy and reducing misinformation. Additionally, BAS dynamically optimizes system parameters and query processing, ensuring low latency even when handling multiple concurrent API calls across diverse languages. Moreover, the system Employs multiple financial data sources, reducing dependence on a single API and strengthening data integrity through cross-validation. These enhancements significantly improve performance, achieving 96.2% accuracy, an F1 score of 0.94, and an Exact Match rate of 92%. With an optimized 0.75-second response time, DeepFinLLM 2.0 provides timely financial insights, including risk assessments and portfolio recommendations. DeepFinLLM 2.0 demonstrates that integrating multilingual NLP, enhanced retrieval, multi-API orchestration, and dynamic BAS tuning within a single architecture can achieve state-of-the-art performance for real-time financial decision-making, offering a scalable solution for institutional and retail investors in dynamic markets.