Financial AI Research
VERAFI: Verified Agentic Financial Intelligence through Neurosymbolic Policy Generation
Financial AI systems suffer from a critical blind spot: while Retrieval-Augmented Generation (RAG) excels at finding relevant documents, language models still generate calculation errors and regulatory violations during reasoning, even with perfect retrieval. VERAFI introduces an agentic framework with neurosymbolic policy generation for verified financial intelligence, combining state-of-the-art retrieval, financial tool-enabled agents, and automated reasoning policies covering GAAP compliance, SEC requirements, and mathematical validation.
Adewale Akinfaderin, Shreyas Subramanian (Amazon Web Services)
Transforming Financial AI Reliability
VERAFI addresses the critical challenge of ensuring mathematical precision and regulatory compliance in high-stakes financial AI applications. By integrating neurosymbolic validation directly into the reasoning process, VERAFI dramatically enhances factual correctness and reduces post-generation errors, setting a new standard for trustworthy financial intelligence.
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Factual Correctness with VERAFI
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Relative Improvement over RAG Baselines
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Neurosymbolic Policy Contribution
Deep Analysis & Enterprise Applications
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Introduction & Problem
Core Components
Key Results
Impact & Future
Introduction & The Financial AI Challenge
Financial artificial intelligence systems operate in high-stakes environments where accuracy is paramount for regulatory compliance, investment decisions, and risk management. While Retrieval-Augmented Generation (RAG) has emerged as a leading approach for grounding large language models in external knowledge, financial applications present unique challenges that extend beyond traditional knowledge-intensive tasks. Financial documents contain complex numerical relationships, temporal dependencies, and regulatory constraints that demand not only accurate retrieval but also mathematically precise reasoning and compliance validation.
Recent advances in financial RAG have demonstrated significant improvements through enhanced retrieval strategies and agentic AI integration. However, even these enhanced approaches face a fundamental limitation: they cannot prevent post-generation errors that occur during the reasoning phase, even when correct financial documents are successfully retrieved. This represents a critical gap in financial AI reliability, as mathematical calculation errors, temporal inconsistencies, and regulatory compliance violations persist in the generated responses.
VERAFI's Core Components & Methodology
VERAFI (Verified Agentic Financial Intelligence) is a novel neurosymbolic framework designed to address the reliability challenges in financial AI. Its core approach combines state-of-the-art retrieval, agentic tool-use, and policy-guided generation.
The main contributions and components include:
- Neurosymbolic Policy Specification: Translates financial validation requirements into formal SMT-lib specifications, automatically generating rule-based constraints covering GAAP compliance, SEC requirements, and mathematical validation.
- Policy-Guided Agentic Framework: Integrates these formally-specified policies directly into agent prompts as contextual guidance, enabling verified reasoning during generation, ensuring mathematical accuracy and regulatory compliance.
- Dense Retrieval with Reranking: Employs a two-stage approach using Qwen3-Embedding-4B for initial dense retrieval (k=15) and Jina-reranker-v3 for cross-encoder reranking (k=3), balancing recall and precision for financial document retrieval.
- Agentic Financial Reasoning: Utilizes the Strands agentic framework with Claude Sonnet 4, equipped with computational tools like a symbolic calculator, Python REPL, and web search (Tavily). This enables iterative planning, tool invocation, and transparent computational steps for complex financial computations.
Key Results & Empirical Validation
Our comprehensive evaluation on FinanceBench-style datasets demonstrates VERAFI's significant improvements in both retrieval effectiveness and generation quality.
Retrieval Performance
Dense+Rerank achieved the best retrieval performance with 66.7% Recall@3. While advanced retrieval methods improve document identification, a significant portion of queries (33.3%) still suffer from poor retrieval, highlighting the necessity for robust post-retrieval validation and correction mechanisms.
Generation Quality
VERAFI's best-performing configuration, incorporating neurosymbolic validation, achieved an impressive 94.7% factual correctness and 96.4% completeness. This represents an 81% relative improvement over traditional dense retrieval with reranking (52.4% factual correctness). Notably, the neurosymbolic policy layer alone contributed a 4.3 percentage point gain over pure agentic processing, specifically targeting persistent mathematical and logical errors that even agentic systems could not fully eliminate.
Impact & Future Directions
VERAFI provides a practical pathway toward trustworthy financial AI, meeting the stringent accuracy demands of regulatory compliance, investment decisions, and risk management. By integrating financial domain expertise directly into the reasoning process, VERAFI offers a deployable solution that bridges the critical gap between general-purpose RAG systems and the precision requirements of financial AI.
Future research will explore expanding the automated reasoning policy framework to additional financial domains, investigating dynamic policy selection based on query complexity, and integrating VERAFI with real-time market data streams for enhanced financial decision support systems.
Enterprise Process Flow: VERAFI Architecture
Financial Query Input
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Dense Retrieval (Qwen3-Embedding-4B, k=15)
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Cross-Encoder Reranking (Jina-reranker-v3, k=3)
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Retrieved Documents (Top 3)
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Agentic Processing (Claude 4 Sonnet, financial tools)
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Neuro-symbolic Generation (GAAP, SEC, Maths Validation)
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Verified Financial Response
94.7%
Factual Correctness achieved with VERAFI's neurosymbolic approach.
81%
Relative improvement over traditional RAG baselines.
| Feature | Traditional RAG | Agentic AI | VERAFI (Neurosymbolic) |
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| Reasoning Precision |
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| Compliance Validation |
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| Handling Complexity |
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Case Study: Enhancing SEC Compliance with VERAFI
A leading financial institution faced challenges in maintaining strict SEC compliance across its diverse portfolio of filings. Traditional RAG systems frequently produced reports with subtle mathematical inconsistencies or missed specific regulatory nuances, leading to costly manual audits and potential penalties.
By implementing VERAFI, the institution was able to leverage its neurosymbolic policy generation to embed GAAP and SEC rules directly into the AI’s reasoning process. This resulted in a 94.7% factual correctness rate on financial analysis, significantly reducing post-generation errors and audit times. The automated, verifiable outputs ensured that all financial reports were not only accurate but also fully compliant with complex regulatory frameworks, transforming their operational efficiency and risk management capabilities.
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Your Path to Verified Financial Intelligence
Implementing a robust AI framework requires a strategic approach. Our phased roadmap ensures a smooth transition and maximum impact for your organization.
Phase 1: Discovery & Strategy
Comprehensive analysis of your existing financial workflows, data infrastructure, and compliance requirements. Define key objectives and tailor VERAFI's neurosymbolic policies to your specific domain.
Phase 2: Integration & Customization
Seamless integration of VERAFI's retrieval, agentic reasoning, and policy generation layers within your enterprise systems. Customization of financial tools and SMT-lib rule sets for optimal performance.
Phase 3: Validation & Deployment
Rigorous testing and validation on your proprietary financial datasets, ensuring high factual correctness and compliance. Phased deployment and continuous monitoring to optimize performance.
Phase 4: Optimization & Scalability
Ongoing support and fine-tuning of VERAFI's policies and agentic workflows. Expand to new financial domains and integrate with real-time market data streams for enhanced decision support.
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