Beyond Text: Structuring Financial Data to Supercharge AI Reasoning

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Author: Denis Avetisyan

New research reveals that grounding large language models in structured knowledge graphs dramatically improves their ability to tackle complex numerical problems within financial documents.

A streamlined pipeline integrates large language models with an inherent knowledge graph to facilitate robust numerical reasoning within financial data.
A streamlined pipeline integrates large language models with an inherent knowledge graph to facilitate robust numerical reasoning within financial data.

Integrating knowledge graphs with large language models yields a 12.3% relative performance gain on the FinQA benchmark for financial question answering.

Accurately extracting and reasoning with numerical data remains a significant challenge for Large Language Models when applied to complex financial documents. This is addressed in ‘Structure First, Reason Next: Enhancing a Large Language Model using Knowledge Graph for Numerical Reasoning in Financial Documents’, which proposes a framework leveraging structured Knowledge Graphs to augment LLM performance. Results demonstrate a 12.3% relative improvement in execution accuracy on the FinQA benchmark by prioritizing structural data representation. Could this approach unlock more robust and reliable automated financial analysis through enhanced numerical reasoning capabilities?

The Challenge of Nuance in Financial Reasoning

Despite demonstrated proficiency in broad linguistic tasks, Large Language Models encounter substantial difficulties when applied to the domain of financial reasoning. This discrepancy arises from the specialized nature of financial data, which frequently involves complex calculations, subtle contextual cues, and an understanding of market dynamics that extend beyond simple pattern recognition. Models often falter not because of a lack of information, but due to an inability to accurately interpret data, discern relevant relationships, and apply appropriate mathematical or logical operations. The nuances inherent in financial language – ambiguous phrasing, implicit assumptions, and dependence on external economic factors – pose a considerable challenge, revealing a gap between generalized language understanding and the precision required for reliable financial analysis. Consequently, tasks demanding rigorous data interpretation and calculation, such as assessing investment risk or forecasting market trends, remain largely beyond the capabilities of current LLM architectures.

Current Large Language Models, despite advancements in natural language processing, demonstrate a notable deficiency in complex financial analysis due to limitations in multi-hop reasoning – the ability to connect disparate pieces of information to arrive at a logical conclusion. Evaluations using the FinQA benchmark reveal an initial Execution Accuracy of only 51.93%, indicating a significant unreliability when faced with tasks requiring sequential inference and the integration of multiple data points. This inability to reliably synthesize financial information hinders the practical deployment of these models in real-world applications, such as investment strategy, risk assessment, and financial forecasting, where accuracy is paramount and even minor errors can have substantial consequences.

The pursuit of increasingly sophisticated financial insights is hampered by fundamental constraints within the architecture of transformer models, despite the prevailing strategy of simply scaling their size. While larger models demonstrate incremental improvements, they rapidly encounter diminishing returns and prohibitive computational costs. This limitation stems from the quadratic complexity of the attention mechanism, which requires processing time and memory that grow proportionally to the square of the input sequence length. Consequently, analyzing lengthy financial reports, complex transaction histories, or interconnected market data becomes exponentially more challenging. This bottleneck restricts the model’s ability to capture long-range dependencies crucial for robust financial reasoning, hindering its capacity to discern subtle patterns, assess risk accurately, and generate trustworthy predictions – ultimately demonstrating that simply making models bigger is not a sustainable path toward true financial intelligence.

Structuring Financial Knowledge with Knowledge Graphs

Knowledge Graphs (KGs) serve as a structured data source to augment Large Language Models (LLMs) for financial reasoning tasks. By representing financial concepts, entities, and relationships in a graph format, KGs provide LLMs with factual grounding beyond the information contained within their training data. This structured representation allows LLMs to perform more accurate inferences, identify inconsistencies, and validate responses against established financial knowledge. The integration of KGs mitigates the risk of LLM-generated hallucinations and improves the reliability of financial analyses, predictions, and decision-making processes. Specifically, KGs enable LLMs to move beyond pattern recognition in text to understanding the semantic relationships inherent in financial data.

Schema-Based Knowledge Graph (KG) Extraction utilizes the Llama 3.1 8B Instruct language model to automatically build a financial KG from both unstructured textual data and structured tabular data. This process involves identifying entities, relationships, and attributes within the source materials and representing them as nodes and edges in the graph. The system is designed to infer schema from the data itself, allowing it to dynamically adapt to varying data formats and content without requiring pre-defined ontologies. Extracted triples, consisting of subject, predicate, and object, are then used to populate the KG, creating a network of interconnected financial concepts and facts. This automated approach reduces the need for manual curation and enables scalability in KG construction for large datasets.

Table Linearization is a necessary preprocessing step for converting relational data within tables into a textual format compatible with Knowledge Graph construction. This process involves systematically extracting data from each cell, row, and column of a table and representing it as a sequence of triples or statements. Specifically, each row is processed to create subject-predicate-object assertions; for example, a table row representing a company’s revenue could be linearized as “Company A – hasRevenue – $10M”. This conversion is critical because Knowledge Graph construction models, such as those based on Large Language Models, operate on textual data. Without linearization, the structured data within tables cannot be effectively incorporated into the Knowledge Graph, resulting in incomplete or inaccurate factual grounding for reasoning tasks. The process ensures all information, including headers and data types, is represented in a textual format for comprehensive capture and integration.

Efficient Retrieval as the Foundation for Augmented Generation

Lightweight retrieval within this framework utilizes a Multi-Layer Perceptron (MLP) to efficiently identify relevant triplets from the constructed Knowledge Graph. This approach filters the graph by scoring potential triplets based on their relevance to the input query, significantly reducing the search space compared to exhaustive graph traversal. The MLP is trained to predict the likelihood of a triplet containing information pertinent to answering the query, enabling rapid access to the most valuable knowledge for subsequent generation. This filtering process minimizes computational cost and latency, making the retrieval process scalable for large Knowledge Graphs and real-time applications.

Retrieval Augmented Generation (RAG) is a technique that integrates the capabilities of Large Language Models (LLMs) with information retrieved from an external knowledge source. LLMs, while proficient in language generation, can sometimes produce outputs lacking factual basis or reflecting information gaps in their training data. RAG addresses this by first retrieving relevant documents or data points from a knowledge base based on the user’s query. This retrieved information is then provided as context to the LLM, allowing it to generate responses grounded in verified facts and reducing the likelihood of hallucinations or inaccuracies. The process leverages the LLM’s generative abilities while ensuring outputs are supported by external, verifiable evidence, ultimately improving the reliability and trustworthiness of the generated text.

Evaluation of the Retrieval Augmented Generation framework was performed using the FinQA benchmark, a dataset consisting of question-answer pairs requiring numerical reasoning based on financial documents. Initial testing utilized the Llama language model as a baseline, achieving an Execution Accuracy of 51.93% on the FinQA dataset. This metric assesses the model’s ability to not only provide a correct answer but also to execute any necessary calculations or data retrieval steps to arrive at the solution, providing a comprehensive measure of performance in a financial question-answering context.

Precision and Robustness: Elevating the Standard in Financial Analysis

The developed framework demonstrably elevates performance in complex financial reasoning. By strategically incorporating structured knowledge, the system achieves an execution accuracy of 58.34% when assessed on the challenging FinQA benchmark. This represents a substantial advancement, exceeding the baseline Llama model by 6.41 percentage points – a relative improvement of 12.3%. The enhanced accuracy isn’t simply a matter of quantity; it signifies a greater capacity to correctly interpret financial data and arrive at logically sound conclusions, offering a more reliable foundation for informed decision-making in financial contexts.

The developed framework demonstrates a substantial advancement in financial reasoning accuracy, achieving a 6.41 percentage-point increase over the foundational Llama model. This improvement isn’t merely incremental; it represents a relative gain of 12.3%, indicating a significantly more effective approach to processing complex financial data. Such gains are crucial for applications demanding precise calculations and reliable insights, as even small errors can have substantial consequences in financial contexts. The observed performance boost suggests the integration of structured knowledge within the framework effectively addresses limitations present in the baseline model, paving the way for more trustworthy and accurate financial analyses.

The framework demonstrates substantial gains in accurately interpreting financial data through improved temporal disambiguation and numerical precision. Recognizing that financial information is heavily time-dependent and often involves complex calculations, the system now minimizes errors arising from misinterpreting dates or inaccurately processing figures. This enhanced reliability isn’t simply asserted, but rigorously validated: Gemini 2.5 Pro serves as an independent judge, assessing the semantic equivalence of the system’s reasoning results against expected outcomes. This automated evaluation process ensures a high standard of quality, moving beyond simple accuracy metrics to confirm that the insights generated are not only correct but also logically sound and meaningfully equivalent to established financial principles, ultimately fostering greater trust in the derived conclusions.

The pursuit of enhanced numerical reasoning, as detailed in this work, echoes a fundamental principle of system design. The study’s success in augmenting Large Language Models with Knowledge Graphs isn’t merely about adding data, but about imposing structure – a deliberate organization that unlocks inherent capabilities. This aligns with the observation of John von Neumann: “The sciences do not try to explain why something happens, they just try to describe how it happens.” The researchers haven’t attempted to fundamentally alter the LLM’s reasoning process, but rather provided a more effective framework – the Knowledge Graph – for accessing and interpreting financial data, demonstrating that a well-defined structure can dramatically improve performance on complex tasks like those found within the FinQA benchmark.

Beyond the Numbers

The observed performance gain through Knowledge Graph integration, while notable, feels less like a destination and more like a re-alignment. It confirms a long-held suspicion: Large Language Models, for all their parametric power, often mistake correlation for comprehension. The structure isn’t emerging from the model; it’s being provided. A truly robust system won’t require external scaffolding to perform basic reasoning – a clever design should inherently prioritize clarity. The current approach feels fragile; a new benchmark, a slightly different data distribution, and the entire edifice could wobble.

Future work must move beyond simply augmenting these models and focus on fundamentally restructuring their internal representations. The challenge isn’t merely retrieving the correct facts, but imbuing the model with an understanding of why those facts matter in a numerical context. This suggests a move toward hybrid systems – models that seamlessly blend symbolic reasoning with parametric learning – a direction that feels less like chasing the next performance spike and more like building something that might actually endure.

The relative simplicity of this approach-a Knowledge Graph-is, ironically, its most compelling aspect. If a design feels clever, it’s probably fragile. A truly elegant solution will be the one that feels almost… obvious in retrospect. The pursuit of increasingly complex architectures often obscures the fundamental truth: structure dictates behavior, and clarity always wins in the long run.

Original article: https://arxiv.org/pdf/2601.07754.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-13 12:05