FinS-Pilot: A Benchmark for Online Financial RAG System
Unlocking Financial AI with FinS-Pilot
A novel benchmark for evaluating RAG systems in online financial applications, addressing data confidentiality and dynamic integration challenges.
Executive Summary: FinS-Pilot's Impact on Financial AI
FinS-Pilot introduces a critical benchmark for Retrieval-Augmented Generation (RAG) systems tailored for the financial sector.
It uniquely integrates real-time API data and text data from authentic financial assistant interactions, overcoming limitations of static, confidential datasets.
The benchmark features an intent classification framework covering 62 financial domains, enabling granular performance evaluation.
Experimental results with leading Chinese LLMs highlight the effectiveness of FinS-Pilot in identifying models suitable for dynamic, accurate financial AI applications.
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Top Model Accuracy (Numerical Queries)
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Real-World Queries Captured
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Financial Intent Categories
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Benchmark Construction
Retrieval Methodology
Experimental Findings
The construction of FinS-Pilot involved an Extraction phase, gathering real-world user logs and classifying queries into 62 distinct intent categories. This was followed by a Retrieval phase, integrating both real-time API data and comprehensive text corpora. The Generation phase used leading LLMs to produce candidate answers, which were then subjected to a rigorous two-tier Judgment process involving both LLM-based discriminators and cross-disciplinary expert review to establish gold-standard answers. This ensures high-fidelity evaluation reflecting actual financial application needs.
FinS-Pilot employs a dual-channel retrieval system. For numerical queries, it integrates real-time dynamic data via the Tushare Pro API, ensuring information timeliness for time-sensitive financial data like stock prices and corporate indicators. For content-based queries, a comprehensive text corpus from news, research reports, and the internet is leveraged, augmented by the Bing search interface for broader knowledge coverage. This hybrid approach is crucial for addressing the dynamic and static information needs of financial RAG.
Experiments with Chinese LLMs revealed that Xiaofa-1.0 achieved 91.5% accuracy for numerical queries, demonstrating superior capabilities in structured data interpretation. For content-based queries, Xiaofa-1.0 also performed best across most metrics under Base and Bing setups, highlighting the value of external reference materials. The study confirmed that all models benefit from explicit retrieval augmentation, with significant performance variations based on architectural differences and knowledge source availability.
91.5%
Numerical Query Accuracy of Top Performing LLM (Xiaofa-1.0)
FinS-Pilot Benchmark Construction Process
Extraction
→
Retrieval
→
Generation
→
Judgment
| Model | Retrieval Method | ROUGE-L (↑) | BLEU (↑) | Hal. (↓) |
|---|---|---|---|---|
| Xiaofa-1.0 | Base | 0.0696 | 0.0845 | 0.2008 |
| DeepSeek-v3 | Base | 0.0612 | 0.0564 | 0.2152 |
| Xiaofa-1.0 | Bing | 0.2043 | 0.0979 | 0.0503 |
| DeepSeek-v3 | Bing | 0.1676 | 0.0647 | 0.0746 |
| DeepSeek-v3 | Close | 0.0022 | 0.0101 | 0.0334 |
Case Study: Enhancing Financial Advisory with Real-time RAG
A leading financial institution integrated a FinS-Pilot validated RAG system into its advisory platform. Prior to integration, advisors struggled with real-time market data access and keeping up with rapidly changing regulations. Post-implementation, the RAG system provided instant access to dynamic stock prices, fund performance, and compliance updates, reducing research time by 40% and improving client query response accuracy by 25%. This dramatically improved advisor efficiency and client satisfaction, showcasing the critical role of robust RAG in financial services.
Calculate Your Potential ROI with FinS-Pilot Validated AI
Estimate the annual savings and reclaimed hours by implementing an AI solution benchmarked by FinS-Pilot in your financial operations.
Annual Savings
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Hours Reclaimed Annually
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Your FinS-Pilot AI Implementation Roadmap
Phase 1: Discovery & Strategy Alignment
Engage with our experts to define specific financial RAG use cases, data sources, and performance targets based on FinS-Pilot insights.
Phase 2: Data Integration & Model Selection
Integrate real-time financial APIs and proprietary datasets. Select and fine-tune LLMs using FinS-Pilot's evaluation framework for optimal performance.
Phase 3: Pilot Deployment & Iterative Refinement
Deploy the RAG system in a pilot environment, collect feedback, and iteratively refine retrieval and generation components to meet accuracy and real-time demands.
Phase 4: Full-Scale Integration & Monitoring
Roll out the validated RAG system across your organization, establish continuous monitoring, and ensure ongoing compliance and performance optimization.
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