FailSafeQA: Evaluating AI Hallucinations, Robustness, and Compliance in Financial LLMs

Audio Overview

A Financial LLM Benchmark like FailSafeQA is essential as AI models are being trusted with billion-dollar decisions, from investment risk assessments to regulatory compliance. Yet, a 2024 study found that LLMs hallucinate in up to 41% of finance-related queries, creating serious risks for enterprises and financial institutions.

However, accuracy and reliability remain major concerns. Unlike structured data tasks, financial AI requires nuanced understanding, contextual reasoning, and factual precision. A minor misinterpretation in financial filings or hallucinated insights can lead to severe consequences, such as misinformed investments, compliance violations, or legal liabilities.

Challenges with LLMs in Financial AI

• Long Context Processing Issues
  Financial reports like 10-K filings span tens of thousands of tokens. LLMs struggle with retaining relevant details while filtering out noise.
• Sensitivity to Query Variations
  Minor differences in wording (e.g., “net income” vs. “earnings”) can impact model outputs. AI can misinterpret spelling errors, fragmented inputs, or ambiguous questions.
• Hallucination Risks in Financial AI
  Unlike structured finance models, LLMs generate free-text responses, increasing hallucination risks. A 2024 study found that AI hallucinations in financial NLP occur in up to 41 percent of cases. Text generation tasks, such as summarization and report writing, are more prone to errors than direct Q&A.
• Compliance and Trust Issues
  Regulatory bodies, including the SEC, FCA, and EU AI Act, emphasize trustworthiness and interpretability in AI. Financial institutions must ensure that LLMs do not mislead analysts, traders, or regulators.

Financial AI must go beyond general NLP benchmarks and assess LLM robustness in real-world financial settings.

Introducing FailSafeQA: A New Financial AI Benchmark

To address these challenges, researchers developed FailSafeQA, a novel benchmark that rigorously tests LLMs in financial question-answering (QA) tasks under real-world failure conditions. Unlike traditional benchmarks that assess models on ideal, well-structured inputs, FailSafeQA evaluates LLMs on imperfect and ambiguous inputs that mirror real-world financial interactions.

What Makes FailSafeQA Different?

• Tests LLMs under challenging scenarios, including typos, missing data, OCR distortions, and domain shifts.
• Focuses on financial Q&A tasks rather than generic NLP benchmarks.
• Measures AI hallucinations and refusals, capturing how models handle uncertainty and factual accuracy.
• Benchmarks both proprietary and open-source models, including GPT-4o, Llama 3, Qwen 2.5, and Palmyra-Fin-128k.

Financial AI must balance accuracy, robustness, and compliance to prevent costly errors. FailSafeQA provides an objective standard for evaluating AI in financial document processing, trading insights, and risk analysis.

Beyond Traditional AI Benchmarks

Existing benchmarks for financial AI, such as FinanceBench, FinBen, and FinD-ABench, primarily assess models under structured and ideal conditions. However, they do not:

• Test how well AI models handle real-world input failures.
• Assess how models react when crucial financial context is missing or distorted.
• Measure the trade-off between robustness (answering despite challenges) and context grounding (avoiding hallucinations).

FailSafeQA fills this gap by introducing a financial AI stress-test framework, ensuring models can handle real-world noise and uncertainty.

Why This Article?

This is not just another LLM benchmark. FailSafeQA was designed to expose hidden risks in financial AI models—risks that could lead to billions in losses.

This article will break down:

• How FailSafeQA replicates real-world financial AI failure scenarios
• The key metrics used to assess AI hallucination risks and compliance
• Which LLMs performed best—and which ones struggled
• Why financial AI must prioritize reliability over mere accuracy

For AI practitioners, financial professionals, and regulators, this benchmark provides a blueprint for ensuring AI-driven decision-making is trustworthy and risk-aware.

Next, we will examine the FailSafeQA dataset and the methodology behind its construction.

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The FailSafeQA Dataset

Data Sources & Scope

FailSafeQA is built on publicly available 10-K filings from U.S. publicly traded companies to make sure that the dataset reflects real-world financial documents that professionals and analysts interact with daily. These filings contain detailed corporate financial data, including revenue reports, risk factors, and management discussions, making them an ideal testbed for evaluating LLMs in financial AI applications.

To introduce temporal variability, the dataset includes 10-K reports from four different years: 1998, 1999, 2017, and 2018. This range allows for testing LLMs across different regulatory environments and financial reporting standards, ensuring that models can handle historical and contemporary financial language.

Handling Long Contexts

Financial documents are often extensive, with some 10-K filings exceeding 100,000 words. To ensure that models process information efficiently while maintaining the structural integrity of financial data, FailSafeQA keeps complete paragraphs intact while capping document length at 25,000 tokens. 

This approach guarantees that:

• LLMs receive enough context to formulate accurate responses.
• The dataset remains consistent with real-world finance scenarios where professionals analyze long reports.
• Models are evaluated on their ability to retrieve key financial insights from large contexts without losing track of relevant details.

Automated Data Pipeline

FailSafeQA leverages an automated pipeline to generate queries and validate responses, guaranteeing that the dataset remains highly structured and scalable.

• Query Generation: The dataset employs Meta Llama 3.1 405B to create multi-turn financial queriesbased on the 10-K reports. These queries simulate how financial professionals interact with LLMs when analyzing complex financial statements.
• Query Filtering: Only well-supported question-answer pairs are retained. The dataset undergoes multiple filtering steps to remove ambiguous, redundant, or weakly supported responses, ensuring high-quality benchmarking.
• Citation Extraction: To ensure factual grounding, LongCite-llama3.1-8b is used to extract supporting citations from the documents. This step enhances model evaluation by verifying whether an LLM’s response aligns with actual data from the financial reports.

Types of Query Perturbations

FailSafeQA introduces three levels of query modifications to assess how LLMs handle real-world user interactions, particularly when input errors or ambiguous phrasing are involved.

• Misspelled Queries: Introduces controlled typos and OCR-induced distortions, mimicking scenarios where users submit queries with minor spelling errors. The benchmark includes:
  • Split Errors: Incorrect segmentation of words (e.g., “netincome” instead of “net income”).
  • Segment Errors: Unintentional breaks in words (e.g., “stoc k price”).
  • Real-Word Errors: Misspellings that create another valid word (e.g., “trade” instead of “trend”).
  • Common Typos: Character swaps, deletions, and insertions based on real-world typo probabilities.
• Incomplete Queries: Simulates keyword-based search engine inputs, testing how well models infer missing information in fragmented queries.
• Out-of-Domain Queries: Evaluates whether models can distinguish between relevant financial topicsand unrelated subject matter, ensuring that LLMs do not fabricate answers.

Types of Context Perturbations

Beyond query variations, FailSafeQA also assesses how LLMs handle incomplete or misleading context—a crucial aspect in financial AI where missing information can lead to false conclusions.

• Missing Context: Certain sections of financial reports are deliberately removed to test whether models can identify gaps in knowledge rather than hallucinate responses.
• OCR Errors Simulation: Introduces scanning distortions, replicating real-world challenges where financial documents are digitized from scanned copies, often with formatting inconsistencies. The errors are modeled using probabilistic functions, including:
  • Character Deletions: Dropping random characters to mimic OCR misreads.
  • Character Insertions: Adding extra letters due to scanning artifacts.
  • Character Substitutions: Changing visually similar characters (e.g., “O” → “0”, “I” → “1”).
• Irrelevant Context: Pairs queries with unrelated financial documents, evaluating whether models can resist drawing conclusions based on incorrect information.

Dataset Composition & Statistics

The FailSafeQA dataset is designed to reflect the complexity and variability of real-world financial data, with the following characteristics:

• 220 examples, each containing both original and perturbed queries.
• Context lengths range from 4,100 to 27,000 tokens, ensuring a broad evaluation of how LLMs handle long-text comprehension.
• 93.64% of test cases feature long-context inputs (exceeding 16,000 tokens), making this one of the most extensive financial QA datasets.
• Task distribution:
  • 83% focused on question answering (QA), testing models’ ability to retrieve precise financial information.
  • 17% focused on text generation (TG), assessing how LLMs summarize or explain financial concepts.
• Diversity Analysis: The dataset contains a broad range of verb-object pairs derived from normalized queries, ensuring that models are tested on a wide variety of financial topics.

By introducing controlled failures, FailSafeQA enables a more rigorous evaluation of financial AI models, ensuring they perform reliably under real-world conditions.

Next, we will examine the evaluation metrics used in FailSafeQA to measure robustness, compliance, and hallucination risks in LLMs.

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FailSafeQA – Evaluation Metrics

Assessing the reliability of Large Language Models (LLMs) in finance requires more than traditional accuracy-based evaluations. Since financial AI applications involve high-stakes decision-making, it is critical to measure how well an LLM responds under uncertainty, avoids hallucination, and balances robustness with factual accuracy.

FailSafeQA introduces a multi-dimensional evaluation framework designed to capture these nuances. The benchmark assesses not just answer correctness but also the model’s ability to handle input perturbations and refuse to answer when necessary.

For a broader discussion on benchmarking LLMs, refer to Benchmarking Large Language Models.

Answer Relevance

FailSafeQA evaluates how well an LLM’s response aligns with the ground truth using a six-point rating scale:

• 1-3: Low relevance, incorrect, or misleading responses.
• 4-6: Relevant and factually correct answers with minimal hallucination.

A score of 4 or higher is considered an acceptable response in financial applications. Unlike traditional benchmarks that use binary accuracy measures, this approach accounts for partial correctness and nuanced financial language.

Answer Compliance

Answer Compliance is a binary metric (c≥4) that evaluates whether a response meets the minimum relevance threshold to be considered factual and useful.

• If a model’s relevance score is ≥4, the answer is marked as compliant.
• If the score is below 4, the response is considered unreliable or misleading.

This metric ensures that LLMs only output reliable information rather than producing speculative or hallucinated answers.

LLM Robustness (R)

Measures how well a model maintains accuracy despite misspellings, incomplete queries, and missing context—a crucial factor for financial AI reliability. This includes handling:

• Misspelled queries
• Incomplete or ambiguous questions
• OCR errors and missing context

An ideal financial AI model should gracefully manage real-world input noise while preserving factual consistency. Models that fail in this category tend to generate incorrect responses when faced with minor query variations, making them unsuitable for critical financial applications.

LLM Context Grounding (G)

Tests whether a model knows when it lacks enough information to provide a reliable answer, ensuring AI does not fabricate financial insights.

• High grounding scores indicate that the model avoids hallucination and does not fabricate information.
• Low scores suggest that the model tends to make up answers even when context is missing or distorted.

In financial applications, this metric is critical since AI-driven decisions must be based on verifiable information, not speculative outputs.

LLM Compliance Score (LLMCβ)

A precision-recall-inspired metric that balances robustness (ability to answer) vs. grounding (ability to detect uncertainty). A β < 1 score means a model prioritizes refusal over hallucination—ideal for risk-sensitive financial applications.

• β < 1: The model prioritizes refusal over hallucination.
• β > 1: The model leans toward providing an answer, even at the risk of hallucination.

Financial AI models should ideally favor a lower β, ensuring that refusal rates remain high when context is inadequate rather than generating misleading outputs.

Why These Metrics Matter in Financial AI

FailSafeQA’s evaluation framework goes beyond generic LLM benchmarks by introducing metrics specifically designed for high-risk environments. In finance, a model that is aggressive yet unreliablecan be just as dangerous as one that refuses to answer too often. By combining relevance, compliance, robustness, and context grounding, this benchmark provides a more holistic view of model reliability.

Next, we will analyze the experimental setup, detailing the LLMs evaluated and how they were tested under FailSafeQA’s framework.

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FailSafeQA – Experimental Setup

To rigorously evaluate LLM performance under financial QA scenarios, FailSafeQA tested models with 128k+ context support, ensuring they could effectively handle long-form financial documents. The evaluation covered both open-source and proprietary LLMs, providing a comprehensive comparison between cutting-edge research models and commercially deployed systems.

LLMs Evaluated

FailSafeQA assessed a diverse set of models, including:

Open-Source Models:

• DeepSeek-R1
• Llama 3.x
• Qwen 2.5
• Nemotron-70B
• Phi 3
• Palmyra-Fin-128k (a finance-specialized model)

Proprietary Models:

• GPT-4o
• OpenAI o1
• OpenAI o3-mini
• Gemini 2.0

This selection makes sure that FailSafeQA provides both an industry-wide benchmark and insights into the performance gap between proprietary and open-source LLMs in financial AI applications.

Judging Method: LLM-as-a-Judge

FailSafeQA employs an LLM-as-a-Judge approach to evaluate responses, using a trusted, high-accuracy model to rate candidate outputs against ground truth citations. The Qwen2.5-72B-Instruct model was chosen as the evaluation model, ensuring that assessments are:

• Consistent: It follows predefined rating criteria based on factual accuracy and financial context grounding.
• Scalable: Automates large-scale model evaluation while maintaining reliability.
• Citation-Aware: Each response is judged on whether it aligns with ground truth sources from the dataset.

This methodology allows FailSafeQA to systematically identify hallucinations, robustness issues, and compliance failures, making it one of the most rigorous financial AI benchmarks to date.

Next, we analyze the results and key findings from the benchmarking process.

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FailSafeQA – Results and Discussion

1. Overall Model Performance

FailSafeQA’s evaluation revealed notable differences in how LLMs approach financial QA tasks, particularly in balancing answer generation with refusal mechanisms.

• Models were generally more likely to answer than to refuse, even when context was insufficient.This behavior presents risks in regulated financial environments, where incorrect responses can have significant consequences.
• Text generation tasks exhibited higher hallucination rates compared to direct question answering (QA). Summarization and free-text generation introduce greater uncertainty, making fact verification critical in financial AI deployment.

2. Robustness vs. Context Grounding Trade-Off

One of the core findings of FailSafeQA is that no model excels in both robustness and context grounding simultaneously. The best-performing models demonstrated a trade-off between:

• Robustness (ability to answer despite input perturbations)
• Context Grounding (ability to detect when an answer is not possible without hallucinating)

Key insights from the evaluation include:

FailSafeQA revealed a critical trade-off between robustness (ability to answer despite imperfect queries) and context grounding (ability to avoid hallucinations).

• OpenAI o3-mini was the most robust, meaning it answered more often despite input distortions. However, this came at a cost—it hallucinated in 41% of cases, making it risky for applications requiring strict factuality.
• Palmyra-Fin-128k demonstrated the best Context Grounding, meaning it was far less likely to generate misleading answers. However, its robustness dropped by 17%, meaning it often refused to answer rather than risk inaccuracies.
• Takeaway: Financial AI models must be selected based on use case—if accuracy is critical, a conservative model like Palmyra-Fin-128k is preferable. If completeness is required, o3-mini’s robustness might be valuable—but with additional safeguards.

These findings indicate that financial AI models must be carefully chosen depending on their intended use case. Models optimized for risk-sensitive applications should prioritize grounding over robustness, whereas AI models designed for search and exploration can afford a higher tolerance for hallucination.

3. Financial AI Risk Insights

The evaluation results highlight several key risks associated with deploying LLMs in financial applications:

• LLMs designed for finance must balance accuracy with intelligent refusal strategies. A model that always answers—even when it lacks relevant context—can mislead analysts, investors, and regulators.
• Models with high hallucination rates could pose legal and financial risks. In regulated industries, AI-generated misinformation could lead to compliance violations and reputational damage for firms relying on LLM-driven financial insights.

FailSafeQA demonstrates that LLMs require fine-tuning and ongoing evaluation before they can be safely deployed in financial environments. The ability to handle real-world failures, recognize limitations, and provide fact-based responses is crucial for ensuring AI-driven financial intelligence remains trustworthy and compliant.

Next, we will explore how FailSafeQA compares to existing financial AI benchmarks and what it reveals about the current state of LLM development in finance.

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Related Work

Financial AI models require rigorous evaluation frameworks to ensure they perform reliably under real-world conditions. FailSafeQA builds on previous work in financial AI benchmarking, LLM robustness studies, and hallucination detection, expanding the scope of evaluation to focus on context perturbations and real-world input variations.

Financial AI Benchmarks

Several existing benchmarks have been developed to assess the performance of AI models in financial tasks. While these benchmarks provide valuable insights, they often focus on structured financial tasks rather than real-world QA challenges.

• FinBen: Evaluates financial document understanding and information retrieval, primarily for structured financial statements and earnings reports.
• FinanceBench: Measures performance on financial sentiment analysis, news summarization, and document classification but does not account for model robustness in long-context QA.
• FinDABench: Focuses on domain adaptation in financial AI, assessing how well models generalize to different financial subfields.
• PIXIU: A benchmark that integrates structured financial data with unstructured text analysis, evaluating AI’s ability to reason over hybrid datasets.

These benchmarks are valuable for structured financial AI tasks but do not fully account for AI performance in long-context document retrieval, hallucination risks, and failure scenarios. FailSafeQA fills this gap by testing models under perturbed conditions that simulate real-world financial queries.

LLM Robustness Research

The broader AI research community has recognized the importance of evaluating LLMs for robustness, reliability, and adaptability. A key effort in this space is:

• HELM (Holistic Evaluation of Language Models): Developed by Stanford, HELM assesses LLMs across multiple domains, emphasizing fairness, bias detection, and reasoning. While HELM provides insights into general-purpose LLM robustness, it lacks domain-specific stress tests for finance, particularly in handling missing or misleading context.

FailSafeQA extends the idea of holistic evaluation by specifically focusing on financial QA under stress conditions, ensuring models can handle complex, noisy, and incomplete financial inputs.

AI Hallucination Studies

Hallucination is a critical issue in financial AI, as models that generate plausible but incorrect information can mislead financial analysts and decision-makers. Several studies have aimed to measure and mitigate hallucination risks:

• HaluEval: A large-scale benchmark for evaluating hallucinations in LLMs, covering fact-checking, citation verification, and misinformation detection.
• Hallucination Leaderboard: Public LLM leaderboard computed using Vectara’s Hughes Hallucination Evaluation Model..
• NLP Robustness Studies: Various academic studies have analyzed LLM robustness to adversarial queries, document distortions, and conflicting context, highlighting the need for context-aware hallucination detection.

While these benchmarks provide valuable insights, they fall short in simulating real-world financial document distortions and user errors.

• They do not test how AI models handle OCR distortions from scanned financial reports.
• They do not evaluate how models respond when critical financial data is missing or misleading.
• They do not measure the trade-off between robustness (aggressive answering) and factuality (avoiding hallucination).

FailSafeQA is the first benchmark explicitly designed to test LLMs in financial failure conditions, making it an essential tool for financial AI validation.

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Related Articles 

• Advancing AI Accuracy with Retrieval Interleaved Generation (RIG) – This article discusses methods to mitigate AI hallucinations by integrating retrieval mechanisms, enhancing the factual accuracy of AI-generated content.
• RARE: Enhancing AI Accuracy in High-Stakes Question Answering – Explores strategies to improve AI performance in critical domains like finance, focusing on reducing hallucinations and ensuring reliable outputs.
• Exploring Agentive AI: Understanding its Applications, Benefits, Challenges, and Future Potential – Examines how agentive AI can enhance decision-making processes in high-stakes industries, including finance, by providing autonomous, reliable assistance.
• Explainable AI: Importance, Techniques, and Applications – Highlights the significance of transparency in AI models, particularly in financial applications, to ensure trust and compliance.

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Conclusion

FailSafeQA introduces a new standard for evaluating financial LLMs, highlighting critical weaknesses that must be addressed before AI can be widely adopted in financial decision-making. The key takeaways from this benchmark include:

• LLMs struggle to balance robustness and context awareness. Models that prioritize answering robustness tend to hallucinate when faced with missing or misleading financial data.
• A high Compliance Score requires balancing robustness with context grounding. Financial AI models must be trained to refuse answering when context is insufficient rather than generating misleading responses.
• Finance-focused LLMs like Palmyra-Fin-128k outperform general-purpose models in accuracy.However, these models trade off robustness for strict factual adherence, making them less effective in answering ambiguous financial queries.
• Proprietary models such as OpenAI o3-mini and DeepSeek exhibit aggressive answering tendencies. These models handle query perturbations well but are more likely to hallucinate in scenarios where context is missing.
• Regulators, financial firms, and AI researchers must integrate robustness testing into AI validation processes. Without compliance-focused AI benchmarks, LLMs risk introducing misinformation into financial workflows.
• Future Work: Expanding FailSafeQA to assess multi-source information aggregation, ensuring models can handle contradictory financial data from different documents.

Financial institutions, AI developers, and regulators cannot afford to ignore AI robustness testing. Without rigorous benchmarks, LLMs could introduce misinformation into financial workflows, mislead analysts, and increase regulatory risks.

FailSafeQA provides a blueprint for ensuring financial AI is not just powerful—but responsible. Financial firms deploying AI models must integrate robustness testing and compliance safeguards before relying on AI-driven insights.

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Key Links

• Research Paper : Expect the Unexpected:FailSafe Long Context QA for Finance
• Authors :Kiran Kamble, Melisa Russak, Dmytro Mozolevskyi, Muayad Ali, Mateusz Russak, Waseem AlShikh
• Dataset: https://huggingface.co/datasets/Writer/FailSafeQA