Beyond Search: Smarter Question Answering for Financial Reports

Author: Denis Avetisyan

A new system leverages the power of large language models and intelligent document retrieval to deliver more accurate answers from complex financial filings.

A query processing pipeline-comprising query rewriting, hybrid search integrating full-text and semantic methods, RRF fusion, optional reranking, and answer generation-demonstrates that incorporating a reranking stage significantly impacts overall performance, as evidenced by comparative ablation studies.

Neural reranking within a Retrieval-Augmented Generation (RAG) framework boosts performance on 10-K reports by 15.5%.

Extracting actionable intelligence from extensive financial documents remains a persistent challenge despite advances in natural language processing. This is addressed in ‘Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis’, which investigates a Retrieval-Augmented Generation (RAG) system for answering questions about S&P 500 reports. The study demonstrates that incorporating neural reranking significantly improves answer correctness-by 15.5 percentage points on the FinDER benchmark-and reduces error rates in financial question answering. Will these refined retrieval strategies and modern language models unlock even greater efficiencies in financial analysis and reporting?

Navigating the Labyrinth of Financial Data

The sheer volume of data embedded within comprehensive financial disclosures, such as 10-K reports, presents a formidable obstacle to efficient analysis. These documents, often exceeding one hundred pages, are densely packed with intricate details, legal jargon, and specialized accounting terminology. This complexity isn’t simply a matter of length; the nuanced language and industry-specific phrasing require significant expertise to decipher accurately. Consequently, pinpointing critical information – identifying risks, assessing performance, or understanding strategic direction – becomes a time-consuming and often error-prone process, even for seasoned financial professionals. The challenge lies not just in finding the data, but in correctly interpreting its meaning within the broader context of the company’s financial health and future prospects.

Conventional keyword searches within financial documents frequently stumble when seeking genuinely insightful answers. These methods operate on a surface level, identifying instances of specified terms without grasping the underlying meaning or the relationships between concepts. Financial language is replete with jargon, complex sentence structures, and subtle contextual cues; a query for “risk,” for instance, might return hundreds of irrelevant mentions if the system cannot differentiate between acceptable risk, mitigated risk, and systemic risk. This limitation proves particularly problematic when analyzing documents like 10-K reports, where critical information is often embedded within lengthy narratives and qualified statements, requiring a deeper understanding of semantics and context to accurately interpret the financial health of an organization.

The escalating volume of financial data, coupled with increasing market complexities, is driving a critical need for automated analytical tools. Traditional methods struggle to efficiently process the sheer scale of reports, filings, and news articles relevant to investment decisions and risk assessment. Consequently, the development of robust question-answering systems is paramount; these systems must not simply retrieve information, but actively reason over vast datasets to synthesize answers, identify trends, and provide actionable insights. Such capabilities extend beyond simple data aggregation, requiring advanced natural language processing and machine learning algorithms to interpret financial jargon, understand contextual nuances, and ultimately, unlock the full potential of available financial intelligence. This shift towards automated reasoning promises to empower analysts, improve investment strategies, and enhance the overall stability of financial markets.

This document processing pipeline converts HTML reports to PDF, then extracts, chunks, and embeds the text for both keyword search via SQLite and semantic search using FAISS.

Retrieval-Augmented Generation: A System for Intelligent Insight

Retrieval-Augmented Generation (RAG) systems address limitations of standalone language models by integrating information retrieval with text generation. These systems first retrieve relevant documents from a knowledge source based on a user’s query. A pre-trained language model, such as a large language model (LLM), then utilizes the retrieved content to synthesize a final answer. This approach allows the system to base its responses on factual data, mitigating the risk of hallucination and improving response accuracy, while still benefiting from the LLM’s ability to generate coherent and nuanced text. The combination enables responses that are both informative and articulate, leveraging the strengths of both retrieval and generative techniques.

Hybrid retrieval combines the benefits of both full-text search and semantic similarity search to improve information retrieval accuracy. Full-text search identifies documents containing the exact keywords from a query, providing high precision but potentially missing relevant documents using different phrasing. Semantic similarity search, utilizing vector embeddings and techniques like cosine similarity, identifies documents with conceptually similar content, even if they lack the exact keywords. By integrating both approaches – typically by combining and re-ranking results from each – the system maximizes both recall and precision in identifying the most pertinent information for a given query.

GPT-4.1 is utilized for query rewriting within the RAG system to improve the accuracy and relevance of retrieved documents. This process involves reformulating the initial user query into a more precise and nuanced representation, addressing potential ambiguities and expanding upon implicit information. The rewritten query is then used to search the document database, increasing the likelihood of identifying genuinely pertinent information. Specifically, GPT-4.1’s capabilities in natural language understanding allow it to identify the core intent of the user’s question and rephrase it in a manner optimized for semantic and keyword-based retrieval methods, thereby mitigating the impact of poorly worded or incomplete queries.

The Foundation: Efficient Search Technologies

Full-text search functionality is implemented using FTS5, a dedicated extension to the SQLite database. FTS5 provides fast and efficient keyword matching against document content, indexing text for rapid retrieval of relevant results. Unlike traditional SQL LIKE queries, FTS5 utilizes a specialized indexing structure optimized for text search, supporting boolean operators, phrase searches, and ranking of results based on relevance. The extension handles tokenization, stemming, and stop word removal, improving search accuracy and performance. FTS5 is integrated directly within the SQLite database, avoiding the overhead of external search engines for many use cases.

Semantic similarity search within the system leverages FAISS (Facebook AI Similarity Search), a library designed for high-performance similarity search and clustering on large datasets of dense vectors. These vectors are generated through Text Embeddings, a process converting textual data into numerical representations that capture semantic meaning. FAISS employs optimized algorithms and data structures, including inverted file indexes and product quantization, to enable rapid identification of vectors – and therefore passages – that are semantically similar to a given query vector, despite potential differences in keyword usage. The library supports both GPU and CPU execution, allowing for scalable performance depending on available resources and dataset size.

The ability to rapidly identify relevant passages within financial reports is critical due to the volume and complexity of these documents. Manual review is impractical given the sheer scale of data; automated search technologies significantly reduce the time required to locate specific information. This speed is achieved by indexing the corpus and employing algorithms that prioritize passages based on keyword matches and semantic similarity to user queries. Efficient search directly impacts research velocity, risk assessment, and the timely extraction of key performance indicators from financial disclosures.

Refining Results: Neural Reranking for Precision

Neural reranking is implemented to refine initial search results by assessing the relevance of each retrieved document to the original query. This process utilizes cross-encoder models, specifically the Jina Reranker v2, which processes the query and document text together to generate a relevance score. Unlike bi-encoder models which encode query and documents independently, cross-encoders allow for a more nuanced understanding of the relationship between the query and each candidate document, enabling a more accurate re-scoring of results based on contextual relevance.

Neural reranking enhances search precision by re-evaluating initially retrieved candidates and re-ordering them based on a more nuanced understanding of query-document relevance. This is achieved through cross-encoder models, which process the query and document together to generate a relevance score. By prioritizing passages with higher scores, the system ensures that results are presented in an order that better reflects their actual relevance to the user’s information need, leading to improved user experience and faster access to pertinent information. Evaluation using the FinDER Dataset indicates a 15.5 percentage point increase in correctness – from 33.5% to 49.0% – when employing neural reranking.

Evaluation using the FinDER Dataset indicates a substantial improvement in correctness rate achieved through the implementation of neural reranking. Specifically, the system attained a 49.0% correctness rate with reranking enabled, representing a 15.5 percentage point increase over the 33.5% correctness rate observed without reranking. This metric quantifies the accuracy of the search results in identifying relevant passages, demonstrating the efficacy of the neural reranking process in prioritizing higher-quality results.

Demonstrating Accuracy and System Performance

The accuracy of generated responses was rigorously assessed through a ‘Correctness Rate’ metric, built upon the established FinDER Dataset – a resource specifically designed for evaluating factual consistency in generated text. This metric moves beyond simple keyword matching, instead demanding a nuanced understanding of the information presented and its alignment with verifiable facts within the dataset. By utilizing FinDER, the evaluation process focuses on determining whether a generated answer is not only grammatically correct, but also factually sound and demonstrably true based on the provided context. This comprehensive approach provides a more reliable measure of system performance, distinguishing between responses that merely appear correct and those that are genuinely accurate and informative.

To rigorously evaluate the generated responses, the study implemented an innovative approach utilizing a large language model (LLM) as an impartial judge. This LLM-as-Judge was tasked with assessing both the factual correctness and the contextual relevance of each answer, moving beyond simple keyword matching to capture nuanced understanding. By leveraging the LLM’s inherent linguistic capabilities, the evaluation process aimed for objectivity, minimizing subjective biases often present in human assessments. The LLM assigned scores reflecting the quality of responses, providing a granular metric for performance analysis and enabling a detailed comparison between systems with and without reranking strategies. This method offers a scalable and consistent framework for evaluating the complex outputs of generative AI models.

Evaluations reveal the system attains a 49.0% correctness rate, marking a substantial 15.5 percentage point gain compared to systems lacking a reranking mechanism. This improvement is accompanied by a notable decrease in demonstrably incorrect responses, falling from 35.3% to 22.5% – a reduction of 12.8 percentage points. The average quality score registered at 6.02, representing a 1.07 point increase, while the proportion of perfectly accurate answers climbed to 13.8%, a gain of 3.6 percentage points; these combined metrics demonstrate a significant enhancement in both the reliability and overall quality of generated responses.

The pursuit of robust financial question answering, as detailed in this work, echoes a fundamental principle of systems design: structure dictates behavior. This research demonstrates how carefully considered architectural components – specifically, neural reranking within a Retrieval-Augmented Generation (RAG) system – can dramatically alter performance. Just as a city’s infrastructure determines its flow, the reranking mechanism reshapes the information pathway, yielding a substantial 15.5 percentage point improvement in correctness when interpreting complex 10-K reports. As Barbara Liskov noted, “It’s one of the dangers of having a really good idea-you start to believe it’s the only idea.” This work highlights the importance of refining existing structures, rather than completely rebuilding them, to achieve optimal outcomes in information retrieval and analysis.

What Lies Ahead?

The demonstrated gains from neural reranking within a Retrieval-Augmented Generation framework are substantial, yet feel less like a destination and more like a realignment. The system’s improved performance on 10-K reports suggests a capacity to parse complex documentation, but this capacity is built upon a foundation of structured data. If the system survives on duct tape – clever prompting and architectural tweaks masking underlying ambiguities in financial language – it is likely overengineered. The true challenge resides not in squeezing more performance from the current paradigm, but in addressing the inherent messiness of the source material.

Modularity, often lauded as a path to control, is an illusion without contextual understanding. A reranker can prioritize relevant passages, but it cannot resolve contradictions, interpret intent, or account for the subtle dance of regulatory compliance. Future work must move beyond surface-level relevance and grapple with the semantic web beneath the text. This requires models capable of reasoning, not just pattern matching, and a willingness to acknowledge that complete transparency in financial reporting remains aspirational.

The pursuit of ever-larger language models offers diminishing returns if the foundational data remains untamed. A more fruitful avenue lies in developing systems that actively seek clarification, challenge assumptions, and integrate external knowledge. The goal should not be to automate financial analysis entirely, but to create a symbiotic partnership between human expertise and artificial intelligence – one where the machine augments, rather than replaces, critical thinking.

Original article: https://arxiv.org/pdf/2603.16877.pdf

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