Beyond Benchmark Accuracy

Financial institutions are rapidly exploring Large Language Models (LLMs) for investment workflows, but trust is the central hurdle to adoption. Standard financial benchmarks (e.g. FinQA, ConvFinQA, etc.) test isolated tasks like question-answering or sentiment analysis, but do not measure whether a model’s output can be safely used in live decision-making. In practice, investment professionals demand answers that are grounded in the client’s own data, computationally correct end-to-end, and compliant with the specific regulatory context (e.g. Indian accounting and securities laws). Crucially, the final deliverable (memo, report, spreadsheet) must be complete and defensible, not just a snippet of text.

Recent studies confirm that high benchmark accuracy does not guarantee trustworthiness. For example, the FinanceBench evaluation shows that even GPT-4 (with retrieval) failed or hallucinated on 81% of its answers to financial QA questions [FinanceBench]. Likewise, comprehensive benchmarks (like FinBen and FinBench) demonstrate that generic LLMs often excel at surface tasks (information extraction, short answers) but stumble on deeper reasoning or longer contexts [FinBen] [ESGenius]. In one evaluation of 50 LLMs on ESG/sustainability questions, state‑of‑the‑art models only achieved ~55–70% accuracy zero-shot, jumping to ~80% only with retrieval augmentation [ESGenius]. These findings underscore that current LLM capabilities, even with powerful models like GPT-4 or Llama-2, are fragile when exactness matters.

Moreover, the rapidly growing trend towards vertical (domain-specific) LLMs suggests that general-purpose models alone cannot fill this trust gap. Domain-specialized finance models (e.g. BloombergGPT) are trained on rich financial corpora and internalize industry terminology and regulations. BloombergGPT (50B parameters) outperformed all prior models on finance tasks without losing general performance [BloombergGPT]. Similarly, recent work on India-focused models (FiMI) shows that fine-tuning on India’s unique payment and regulatory context yields large gains: FiMI improved financial reasoning benchmarks by 20% (over a general LLM) and dramatically boosted performance on domain-specific tool-calling [FiMI]. These examples highlight that vertical LLMs leverage specialized training data to achieve higher accuracy and compliance in critical areas of finance [BloombergGPT] [FiMI].

This whitepaper proposes that enterprise trust in financial LLMs depends on four key hypotheses (grounding, computation, jurisdiction, deliverable readiness), which go beyond traditional benchmarks. We outline a production‑oriented evaluation framework that includes a broad financial corpus, synthetic workload generation (via multiple LLMs), and rigorous SME validation. In this framework, models are tested on end-to-end tasks mirroring real investment workflows (from data ingestion to report drafting), with standard prompts so that all models face identical challenges. We then evaluate grounding (does the answer cite real data or hallucinate?), numerical correctness (are complex fund calculations exact?), jurisdictional correctness (does it follow Indian GAAP and regulations?), and deliverable quality (is the output complete and professional?).

By coupling these hypotheses with both automated metrics and expert review, our framework aims to reveal the trust gap between public leaderboard scores and real-world reliability. This approach will surface failure modes that public benchmarks miss, guiding organizations in selecting or fine-tuning LLMs for safe, production‑ready financial AI.

Large Language Models (LLMs) such as GPT-4 have shown remarkable versatility across many domains. However, the finance sector presents unique challenges. Financial tasks are highly regulated and often involve intricate calculations over large documents. Moreover, the cost of errors is far greater: a misleading model suggestion could affect investment decisions worth millions. Yet most LLM benchmarks to date focus on academic tasks: short QA, named entity recognition, or sentiment analysis on news text. While these are useful, they do not capture the trust requirements of enterprise finance.

In particular, industry benchmarks typically assume open-domain knowledge or U.S.-centric data. For example, existing financial benchmarks (PIXIU, FinanceBench, BizBench, etc.) cover only a narrow range of tasks (mostly extraction and QA on US filings) [FinBen]. They lack tests for multi-step fund math, region-specific regulations (e.g. India’s Ind AS, SEBI rules), and the generation of professional deliverables. As FinBen’s creators note, prior benchmarks “primarily focus on financial NLP tasks” and are insufficient to evaluate LLMs across complex financial applications like forecasting, risk management, and decision-making [FinBen]. In short, these datasets miss the critical properties that determine whether an investment professional can rely on an LLM’s output.

Meanwhile, the LLM field is increasingly embracing domain specialization. Major institutions are developing financial LLMs, and research shows this can yield substantial improvements. BloombergGPT’s results confirm that a 50B model trained on finance-specific data “outperforms existing models on financial tasks by significant margins without sacrificing performance on general benchmarks” [BloombergGPT]. Similarly, India-specific models (like FiMI) incorporate local language, regulations, and transaction data to handle India’s unique finance ecosystem [FiMI]. These efforts highlight that general models alone are not enough: To achieve enterprise-grade performance, LLMs must internalize industry knowledge and compliance requirements through specialized corpora.

Despite this progress, a methodological gap remains. How can organizations assess whether a given LLM (general or vertical) is truly ready for deployment in investment workflows? We argue that we must look beyond accuracy numbers on public benchmarks. Instead, we need an evaluation framework guided by business-critical hypotheses about trust. These hypotheses will explicitly test the model’s grounding in data, its mathematical rigor, its adaptation to local regulations, and its ability to deliver complete analyses. The remainder of this whitepaper outlines such a production-focused benchmark framework, along with the synthetic data and expert review process required to implement it.

Investment professionals do not just ask factual questions; they make high-stakes decisions based on synthesized analyses. Key differences from academic NLP tasks include:

• Grounding vs. Hallucination:Finance requires verifiable answers. If a model fabricates a figure or cites the wrong source, it erodes trust immediately. For instance, a model might answer a question correctly in general, but when answering strictly from a client’s documents it could hallucinate facts [FinanceBench]. In FinanceBench’s open-book tests, even GPT-4 with a retrieval step failed to answer correctly 81% of the time (often due to hallucinations or skipping the question) [FinanceBench]. This shows that grounding answers in actual data (and checking them) is essential.
• Multi-step Computation: Real-world finance often involves multi-step calculations (e.g. fund MOIC, XIRR over cash flows, NAV roll-ups). Even if an LLM knows the formulas, chaining them correctly over client data is non-trivial. Prior work on financial QA (FinQA, ConvFinQA) shows that LLMs can handle single-step problems modestly well, but struggle as complexity grows [Srivastava et al.]. In our internal experience, we have seen cases where GPT-4 answers individual calculation questions correctly but completely breaks down on end-to-end portfolio math. No existing public benchmark tests these fund-level multi-hop calculations.
• Jurisdiction & Domain Fit:Most LLMs and benchmarks have been developed on U.S. or global data. But India’s financial context is different: local accounting rules (Ind AS vs. IFRS), tax laws, regulatory mandates (SEBI, Companies Act, FEMA), terminology, even language usage. A model could score highly on U.S. filings yet fail on India-specific queries (e.g. computation of face value vs. market price distinctions, or using the correct fiscal year for Indian tax). Indeed, our team believes that vertical specialization is crucial here. Domain-specialized LLMs that include local regulations in training have shown significant gains; for example, a model fine-tuned on Indian payment data (FiMI) substantially outperformed its generic base on domain tasks [FiMI]. This suggests that one cannot assume global benchmarks imply local readiness.
• Deliverable Readiness:Finally, what clients ultimately need are complete deliverables: memos, slide decks, reports, not just answers to isolated questions. A model could ace multiple-choice or short-answer tests but still produce a disorganized or incomplete report. There is currently no public benchmark that measures a model’s ability to generate end-to-end investment content. Thus, we include “deliverable readiness” as a hypothesis: Can the model compile its analysis into a coherent document that professionals can actually use? This encompasses writing style, format, completeness, and the ability to cite sources.

In summary, an investment-grade LLM must ground its answers in data, compute correctly, respect local finance rules, and assemble the full output. None of these is fully captured by existing benchmarks. The following hypotheses formalize these concerns:

We define four hypotheses (H1–H4) that any evaluation must address:

• H1. Grounding & Trustworthiness:Even if an LLM has high accuracy on open-domain finance QA, it may still hallucinate or misattribute facts when forced to answer from specific documents. We expect a gap between “benchmarks with retrieved evidence” and real enterprise use. For example, FinanceBench found that all current models exhibit hallucination vulnerabilities, none are completely safe [FinanceBench]. A model that answers correctly in general (e.g. GPT-4 on FinQA) can still fabricate numbers on a new document and lose user trust. Thus we will test each model’s answers against the client’s own documents to ensure they truly quote and cite evidence.
• H2. Multi-step Computation Risk: Financial calculations are hierarchical and interdependent. A model can get simple textbook math right (e.g. computing CAGR for one quarter) but still screw up an end-to-end fund NAV calculation spanning many periods and line items. This risk is untested by most benchmarks. Prior studies [Srivastava et al., 2024] show that LLMs often falter on multi-hop numerical problems embedded in tables. We hypothesize that as the number of computation steps increases, accuracy will drop sharply. We will include specialized numerical tasks (XIRR, MOIC, NAV roll-forward, debt schedules) in our evaluation to measure this failure mode.
• H3. Domain/Jurisdiction Alignment (India-specific):A model trained or benchmarked only on global/U.S. data will not reliably handle India-specific finance requirements. For instance, Indian accounting standards (Ind AS) and regulatory disclosures (e.g. Companies Act schedules, RBI guidelines) differ in important ways from U.S. GAAP. We predict that LLMs will show a disproportionately large drop in accuracy on India-focused queries compared to their “headline” benchmark scores. This is supported by domain adaptation research: e.g., FiMI, an Indian-finance LLM, was explicitly trained to internalize regional rules and terminologies, yielding large performance gains in that domain [FiMI]. Our evaluation will therefore include tasks on Indian financial regulations and localized reporting formats to test this hypothesis.
• H4. Deliverable Readiness:Clients demand polished outputs (reports, decks, etc.), not raw Q&A. Even a strong model might fail to produce a well-structured, defensible deliverable. For example, it might omit steps, misorder sections, or fail to cite sources properly in a draft memo. This kind of end-to-end writing is not captured by current metrics. We will test models on complete tasks (e.g., “write a fund pitchbook given data”), and evaluate the results for completeness, factual correctness, and professionalism. This also covers whether the model sensibly refuses out-of-scope requests or resists prompt manipulation, important trust aspects sometimes called “judgment and boundaries.”

Additionally, we note that vertical LLMs (domain-specialized models) are especially relevant under H3. Because India’s market and regulatory environment are unique, a generic model may lack necessary knowledge. Evidence from other sectors shows vertical models trained on the right data can outperform general LLMs on domain tasks [BloombergGPT] [FiMI]. Thus, part of our analysis will be to compare generic versus finance-specialized (and, where possible, India-tailored) models on these hypotheses.

The key point is that we expect models to fail in systematic ways on these four axes, even if their public benchmark accuracy is high. By making these hypotheses explicit, our benchmark can pinpoint the trust gaps and guide remediation.

To motivate our approach, we briefly survey representative benchmarks:

• FinanceBench (PatronusAI) [1]– A 10K-question open-book QA suite over public company data. As discussed, FinanceBench finds that state-of-the-art LLMs hallucinate or refuse on most queries, even with retrieval. It emphasizes the need for grounding.
• FinBen (Xie et al. 2024) [2]– A large benchmark covering 24 tasks (IE, QA, summarization, forecasting, etc.) across diverse datasets. FinBen reveals that LLMs perform well on simple tasks (like information extraction) but struggle with advanced reasoning and forecasting. For example, GPT-4 excelled at stock trading simulations and news IE, whereas other models were better at text generation or alternative tasks. Importantly, FinBen authors note that prior benchmarks (including FinanceBench) had only a few tasks, mostly NLP in nature, and that richer evaluation (including risk management, decision-making) is needed.
• DocFinQA (Reddy et al. 2024) [3]– Extends FinQA to very long documents (100K+ tokens). It shows that LLMs can handle small excerpts but break down on full-length SEC filings, highlighting a need for better long-context handling.
• ESGenius (He et al. 2025) [4]– Focused on sustainability/ESG knowledge. This benchmark found that no current model “understands” ESG adequately in zero-shot (only ~55–70% accuracy) and that retrieval-augmented setups significantly improve accuracy. It demonstrates both the difficulty of interdisciplinary finance topics and the value of grounding.

Other notable efforts (FLUE, FinQA, TAT-QA, etc.) cover specific subtasks like numerical QA or NER. There are also model-specific resources like BloombergGPT’s internal test suite (not public) and open datasets like FinBERT for sentiment. However, none of these capture the full workflow of an investment analyst, especially in an Indian context.

The table below (adapted from FinBen) illustrates that existing benchmarks either cover only limited task sets or focus on single categories, whereas enterprise finance requires multi-step tasks, multi-lingual/regional coverage, and generative outputs:

Table 1: Comparison of existing financial benchmarks by scope. (Sources: FinBen [FinBen], FinanceBench [FinanceBench], ESGenius [ESGenius], etc.)

None of the above tests include jurisdiction-specific rules or full deliverable generation. We therefore design a new evaluation framework that builds on these ideas but extends them in breadth and realism.

Our proposed framework is structured as follows:

1. Financial Corpus– A large repository of authentic enterprise finance documents (transactional data, filings, equity research reports, etc.) spanning asset classes (equities, credit, VC, real assets) and including both global and India-specific content. This corpus is the source of truth for grounding. It includes, for example, real SEC filings and Indian Companies Act disclosures, investment memos, valuation spreadsheets, and relevant regulatory texts. These documents serve as the “closed book” knowledge base for evaluation.
2. Synthetic Task Generation– We generate evaluation instances using multiple frontier LLMs (e.g. GPT-4, Claude, Llama) as “task simulators.” By prompting them over our corpus, we produce diverse question-and-answer pairs, analysis prompts, or scenario descriptions. For example, we might instruct an LLM to generate a due-diligence memo given a set of company filings, or a multi-step financial question based on an annual report table. Using multiple models and prompt styles increases variety and mimics the wide range of real queries. Each candidate instance is then reviewed (see next step) before inclusion. All synthetic tasks and answers are stored in a version-controlled benchmark repository, ensuring reproducibility.
3. SME Validation and Curation– Every generated instance is vetted by financial experts (analysts, consultants, CFA charterholders). They check: Does the question make sense? Is the answer correct and well-justified? Does it cover the intended hypotheses (e.g. India regulation)? They may correct numeric answers, adjust phrasing, or label “unanswerable” where appropriate. The experts also ensure the result is enterprise-relevant: the final query-answer pair (or memo outline, etc.) should resemble a real deliverable. This process yields a high-quality gold standard set of tasks, each annotated with one or more gold answers (numeric or textual) and source citations.
4. Evaluation Protocol– All models are evaluated using standardized prompts. We avoid elaborate prompt engineering to ensure fairness: each model receives the same instructions per task. Models are allowed the entire relevant context (via retrieval or long context) but must answer the query directly. We do not craft model-specific prompts; this controls for prompt skill and focuses on model capability.
5. Scoring and Metrics– We combine automated and human evaluation. For precise answers (numbers, dates), we use exact match or tolerance checks. For longer text outputs (memos, summaries), we may use pretrained critic models (e.g. DeepEval) and curated rubrics. Key metrics include factual accuracy (against gold answers), evidence-citation alignment (did the model cite the correct data?), reasoning soundness, and presentation quality (clarity, completeness). Crucially, we capture metrics tied to our hypotheses: e.g., a hallucination score, error rate on computation steps, and a quality score for deliverables (possibly via a separate expert blind review).
6. Benchmark Taxonomy & Dashboard– We organize tasks by enterprise workflow (e.g. Equity Valuation, M&A Analysis, Portfolio Monitoring) rather than by academic NLP categories. After evaluation, results are reported not only as single overall scores but as sliced by capability (grounding, math, India-specific, deliverable quality) and difficulty. A live dashboard tracks each model’s performance across these dimensions. Benchmarks are versioned, with quarterly updates and regression checks (to ensure a model update doesn’t accidentally hurt a critical capability).

In summary, this framework creates a production-style evaluation: models solve end-to-end finance tasks, and we measure exactly the properties investment professionals care about. It extends beyond current benchmarks by explicitly testing grounding, long-context QA, numerical pipelines, regulatory domain knowledge, and content generation. The use of synthetic data + SME review allows broad coverage while retaining accuracy of labeling.

• Islam et al., FinanceBench: A New Benchmark for Financial Question Answering (2023).
• Xie et al., FinBen: An Holistic Financial Benchmark for LLMs (2024).
• He et al., ESGenius: Benchmarking LLMs on ESG Knowledge (2025).
• Wu et al., BloombergGPT: A Large Language Model for Finance (2023).
• Reddy et al., DocFinQA: A Long-Context Financial Reasoning Dataset (2024).
• Srivastava et al., Evaluating LLMs’ Mathematical Reasoning in Financial Document Question Answering (2024).
• NPCI, FiMI: A Domain-Specific Language Model for Indian Finance Ecosystem (2026).
• Xie et al., FinNLP-AgentScen-2024 Shared Task: Financial Challenges in Large Language Models (2024).
• Chen et al., FinQA: A Dataset of Numerical Reasoning over Financial Data (2021).
• Chen et al., ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering (2022).
• Liang et al., Holistic Evaluation of Language Models (HELM) (2022).
• Wang et al., MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark (2024).