Can AI Handle Business Finances?

Author: Denis Avetisyan

A new benchmark tests large language models’ ability to perform complex financial tasks using real-world data and online tools.

BizFinBench.v2 establishes a comprehensive evaluation framework encompassing eight foundational and two online tasks-distributed across four distinct scenarios-to assess financial reasoning capabilities, as exemplified by the real-time Portfolio Asset Allocation task it facilitates.

BizFinBench.v2 provides a unified, dual-mode, bilingual evaluation of expert-level financial capability in large language models.

Despite rapid advances in large language models, evaluating their true efficacy in complex financial applications remains a significant challenge due to limitations in existing benchmarks. To address this, we introduce BizFinBench.v2: A Unified Dual-Mode Bilingual Benchmark for Expert-Level Financial Capability Alignment, a large-scale evaluation grounded in authentic business data from both Chinese and U.S. equity markets, incorporating both static and real-time online assessment. This benchmark-comprising nearly 30,000 expert-level Q&A pairs-reveals a substantial gap between current LLM performance and that of financial professionals, while highlighting DeepSeek-R1’s relative strength in online tasks. Will this rigorous, business-focused evaluation accelerate the development of LLMs truly capable of transforming financial operations?

The Inherent Fragility of Financial Reasoning

Large language models, despite demonstrating remarkable proficiency in various natural language tasks, consistently falter when applied to the intricacies of financial analysis. This stems not from a lack of data processing capability, but from the fundamentally different reasoning demands of finance – requiring not just pattern recognition, but an understanding of causal relationships, risk assessment, and the interplay of economic factors. Traditional LLMs excel at identifying correlations within datasets, however, they often struggle to extrapolate beyond those observed patterns when faced with novel market conditions or incomplete information. Accurate financial forecasting and decision-making necessitate a deeper comprehension of underlying principles – something that current models, trained primarily on textual data, often lack. Consequently, even seemingly successful predictions can be based on spurious correlations, leading to unreliable and potentially costly outcomes.

Truly assessing the capabilities of large language models in finance necessitates benchmarks that delve beyond superficial pattern recognition. Current evaluation methods frequently reward models for identifying correlations within datasets without verifying comprehension of underlying financial principles – a system can ‘learn’ that stock prices often rise after positive news without understanding why. Effective benchmarks must therefore incorporate tasks requiring genuine financial reasoning, such as interpreting complex financial statements, evaluating investment risks, or predicting the impact of economic events. These assessments should test not just the ability to recall information, but to apply foundational financial knowledge to novel situations and justify conclusions with sound reasoning – moving the focus from statistical accuracy to demonstrable understanding, and ultimately paving the way for AI capable of informed financial decision-making.

Current methods for assessing artificial intelligence in finance frequently stumble on the issue of realistic evaluation data. Many benchmarks utilize artificially generated datasets, simplifying complex financial scenarios to the point where models can achieve high scores through pattern recognition rather than genuine understanding. This reliance on synthetic data fails to capture the nuances of real-world markets – the unpredictable volatility, the impact of macroeconomic factors, and the subtle interplay of human behavior. Consequently, AI that performs well on these benchmarks often struggles when applied to actual financial challenges, hindering progress toward truly capable financial AI systems and creating a disconnect between reported performance and practical utility. A move towards benchmarks grounded in complex, real-world data is therefore crucial for fostering innovation and building trustworthy financial AI.

Truly robust evaluation of financial AI necessitates a framework extending beyond static knowledge assessments to encompass the volatile nature of live markets. Current benchmarks frequently test recall of financial definitions or application of formulas, but fall short in gauging a model’s ability to adapt to unforeseen events or interpret nuanced market signals. A comprehensive system would therefore simulate real-time data streams, incorporating factors like news sentiment, order book dynamics, and macroeconomic indicators. This allows researchers to assess not only whether a model knows financial principles, but how effectively it applies them in a constantly evolving environment, identifying vulnerabilities and fostering the development of genuinely intelligent financial tools capable of navigating complexity and mitigating risk.

This task assesses an LLM’s ability to analyze comprehensive financial reports and rank companies within an industry based on integrated financial data.

BizFinBench.v2: Grounding Evaluation in Reality

BizFinBench.v2 distinguishes itself from prior financial benchmarks by utilizing genuinely observed data sourced from both Chinese and U.S. equity markets. Existing benchmarks often rely on synthetic or simplified datasets, which fail to accurately reflect the complexities and nuances of real-world financial scenarios. This benchmark incorporates actual financial reports, stock prices, and market transactions to provide a more realistic and challenging evaluation environment. The inclusion of data from two major global markets – China and the U.S. – enhances the generalizability and robustness of models tested on BizFinBench.v2, allowing for comparative analysis across different economic contexts and regulatory frameworks.

BizFinBench.v2 evaluates financial reasoning capabilities through two distinct task categories. Offline Tasks focus on static analysis and require models to demonstrate understanding of core financial principles; these include challenges such as interpreting financial reports and performing quantitative computations based on provided data. Conversely, Online Tasks simulate dynamic market scenarios, demanding models to process real-time information and make decisions in response to changing conditions, exemplified by tasks like stock price prediction and portfolio asset allocation. This dual structure allows for a comprehensive assessment, evaluating both foundational knowledge and the ability to apply that knowledge in practical, time-sensitive contexts.

Offline tasks within BizFinBench.v2 are designed to rigorously evaluate a model’s ability to perform complex financial analysis, specifically through challenges in Financial Report Analysis and Financial Quantitative Computation. Financial Report Analysis requires models to interpret data from income statements, balance sheets, and cash flow statements to derive key financial ratios and assess company performance. Financial Quantitative Computation tasks assess proficiency in applying mathematical and statistical techniques to solve financial problems, including calculations related to present and future value, risk assessment, and investment valuation. These tasks necessitate deep analytical skills and a comprehensive understanding of financial principles, moving beyond simple pattern recognition to require reasoned, data-driven conclusions.

Online tasks within BizFinBench.v2 are designed to assess a model’s ability to function in realistic, time-sensitive financial scenarios. These tasks, specifically Stock Price Prediction and Portfolio Asset Allocation, necessitate processing and responding to constantly changing market data. Models are evaluated on their performance as new information becomes available, mirroring the demands of live trading environments. This dynamic evaluation differs from static benchmarks and emphasizes a model’s adaptability and responsiveness to market fluctuations, providing a more practical measure of real-world applicability.

BizFinBench.v2 provides a substantial evaluation resource through its large-scale dataset of 29,578 question-answer pairs. This volume is intended to facilitate more robust and comprehensive assessment of financial reasoning capabilities in language models. The dataset’s size allows for statistically significant performance comparisons and reduces the impact of chance occurrences in benchmark results. Each question-answer pair has been constructed to test specific financial skills, contributing to a detailed performance profile for evaluated models, and enabling granular analysis of strengths and weaknesses across diverse financial tasks.

The Stock Price Prediction task assesses an LLM’s ability to forecast individual stock closing prices by analyzing and summarizing the influence of fundamentals, technical indicators, and market sentiment.

Dissecting Foundational & Real-Time Financial Acumen

The Offline Tasks within the BizFinBench.v2 benchmark are designed to evaluate a language model’s core financial intelligence. These tasks specifically assess Business Information Provenance – the model’s ability to accurately identify the origin and reliability of financial data – and Financial Logic Reasoning, which tests its capacity to apply established financial principles to solve problems. Evaluation focuses on static datasets, isolating the model’s inherent understanding of financial concepts independent of real-time market factors or interactive dialogue. Successful completion of these tasks requires not simply recognizing financial terms, but demonstrating a functional comprehension of their relationships and implications within established financial frameworks.

Anomaly Information Tracing and Financial Data Description tasks within BizFinBench.v2 assess a model’s capacity to validate the accuracy and relevance of financial data. Anomaly Information Tracing requires identifying inconsistencies or outliers within provided datasets, demanding verification against source information or established financial principles. Financial Data Description tasks, conversely, focus on a model’s ability to accurately summarize and contextualize specific data points, effectively translating raw data into meaningful, interpretable information. Successful completion of these tasks demonstrates a model’s foundational understanding of data integrity and its ability to extract and present pertinent financial details.

The Online Tasks within BizFinBench.v2 assess Large Language Model (LLM) performance in dynamic financial scenarios. These tasks necessitate Real-time Market Discernment, evaluating a model’s ability to interpret and react to shifting market data, including price fluctuations and news events. Simultaneously, the benchmark measures Stakeholder Feature Perception, requiring LLMs to understand how different actors – such as investors, regulators, and company management – might interpret the same financial information, and to tailor responses accordingly. Successful completion of these tasks demonstrates an LLM’s capacity to function effectively in a continuously evolving financial landscape.

Financial Multi-turn Perception tasks within BizFinBench.v2 assess a language model’s ability to process and retain information across multiple conversational turns related to financial scenarios. These tasks present a series of interconnected questions or requests, requiring the model to maintain context from previous interactions to provide accurate and relevant responses. Evaluation focuses on the model’s capacity to correctly interpret subsequent queries based on established context, rather than treating each turn as an isolated input, thereby simulating realistic financial dialogues and assessing sustained comprehension.

Evaluations conducted using the BizFinBench.v2 benchmark demonstrate performance differences between large language models. Specifically, ChatGPT-5 achieved an overall accuracy of 61.5% across all tasks within the benchmark. In comparison, the Qwen3-235B-A22B-Thinking model averaged 53.3% accuracy on the same BizFinBench.v2 tasks. These results provide a quantitative comparison of the models’ abilities in financial reasoning and perception, as assessed by the benchmark’s established metrics.

The Counterfactual Inference task challenges large language models to emulate financial experts by reasoning about the potential outcomes of hypothetical scenarios based on existing industry data and policy.

Nuanced Reasoning: Unpacking Logic, Events, and Counterfactuals

Event Logic Reasoning within BizFinBench.v2 evaluates a model’s capability to correctly order financial events and identify causal links between them. These tasks present scenarios requiring the model to determine the temporal sequence of actions – such as a loan application, approval, and disbursement – and to understand how one event directly influences another. Assessment involves verifying if the model can accurately establish relationships like how an increase in interest rates impacts bond prices, or how a company’s earnings report affects its stock valuation. The complexity lies in distinguishing correlation from causation and handling nuanced financial contexts where multiple factors contribute to an outcome, necessitating a deep understanding of financial principles and logical inference.

Counterfactual Inference tasks within BizFinBench.v2 present Large Language Models (LLMs) with hypothetical financial scenarios, requiring them to predict outcomes based on altered conditions. These tasks move beyond simple pattern recognition by demanding that models reason about “what if” situations – for example, assessing the impact of a different interest rate on a loan’s performance, or predicting portfolio returns given an alternative investment strategy. Evaluation focuses on the accuracy of these predictions, testing the model’s ability to understand causal relationships and apply financial principles to non-observed circumstances. Performance on these tasks is crucial for evaluating a model’s capacity for proactive financial analysis and risk assessment.

BizFinBench.v2 assesses model capabilities in User Sentiment Analysis by presenting scenarios requiring the interpretation of stakeholder opinions – including customers, analysts, and regulators – and their projected influence on financial outcomes. This evaluation extends beyond simple sentiment detection to focus on the impact of these perceptions on key financial decisions, such as investment strategies, risk assessments, and credit evaluations. The framework utilizes datasets containing textual data expressing various stakeholder viewpoints, demanding that models accurately gauge sentiment polarity and intensity, and subsequently, correlate these sentiments with plausible financial consequences. Performance is measured by the model’s ability to predict how shifts in user sentiment would realistically affect financial variables within the given context.

BizFinBench.v2 employs Zero-Shot Evaluation to assess a model’s capacity to respond to financial reasoning tasks without prior task-specific training. This method tests inherent reasoning abilities. Complementing this, the benchmark utilizes Chain-of-Thought (CoT) prompting, a technique where models are encouraged to articulate their reasoning process step-by-step. By analyzing the generated chain of thought, researchers can evaluate not only the final answer but also the depth and validity of the model’s reasoning, providing a more granular understanding of its performance beyond simple accuracy metrics. This combination of evaluation techniques aims to provide a rigorous assessment of financial reasoning capabilities in Large Language Models.

Evaluation of Large Language Models on the BizFinBench.v2 benchmark demonstrates varying levels of performance; Dianjin-R1 achieved an overall accuracy of 35.7%, indicating a significant challenge for current models in complex financial reasoning. While accuracy provides a general measure of correctness, DeepSeek-R1 currently exhibits superior performance when evaluated using the Sharpe Ratio, a metric that assesses risk-adjusted returns and is crucial for financial decision-making. This discrepancy suggests that models may achieve correct answers without necessarily optimizing for financially sound strategies, necessitating evaluation with multiple metrics.

The Event Logical Reasoning task evaluates an LLM’s ability to apply logical reasoning and domain expertise by ordering financial events chronologically in response to a user query.

Towards More Robust and Reliable Financial AI

BizFinBench.v2 addresses a critical need in the rapidly evolving field of financial artificial intelligence: a consistent and thorough method for assessing model performance. Prior to its development, evaluating these complex systems proved challenging due to the lack of standardized datasets and metrics, hindering meaningful comparisons and progress. This benchmark provides a unified evaluation framework, encompassing a diverse range of financial tasks and real-world scenarios, allowing researchers and developers to rigorously test and refine their AI models. By offering a common ground for assessment, BizFinBench.v2 not only accelerates innovation but also fosters greater trust in these increasingly important financial tools, ultimately paving the way for AI capable of informed financial decision-making.

BizFinBench.v2 distinguishes itself through a commitment to evaluating financial AI models using data mirroring genuine, complex financial landscapes. Unlike benchmarks relying on synthetic or simplified datasets, this framework utilizes authentic business reports, transaction records, and market data-sources that inherently contain the noise, inconsistencies, and nuances of real-world financial challenges. This approach ensures that models demonstrating strong performance on BizFinBench.v2 are not merely excelling at contrived tasks, but possess a demonstrable capacity to handle the complexities encountered in practical financial applications, such as credit risk assessment, fraud detection, and financial forecasting. Consequently, the results obtained provide a more trustworthy indicator of a model’s potential for successful deployment and impactful contribution to financial decision-making.

BizFinBench.v2 doesn’t simply assess existing financial AI; it actively illuminates pathways for improvement. Through detailed analysis of model performance across diverse financial tasks, the benchmark reveals which prompting strategies elicit the most accurate and reliable responses. This granular insight extends to model architecture, highlighting strengths and weaknesses in various approaches to financial data processing. Consequently, developers can leverage these findings to refine existing models or design novel architectures specifically tailored to overcome identified limitations. The benchmark’s data-driven feedback loop promises to accelerate progress beyond incremental gains, fostering a new generation of financial AI systems built on a foundation of empirical evidence and optimized for real-world performance.

The establishment of a standardized benchmark in financial AI, such as BizFinBench.v2, is poised to dramatically reshape the landscape of financial technology and decision-making processes. By providing a common ground for evaluating model performance, this benchmark fosters direct comparisons and accelerates the pace of innovation, enabling researchers and developers to rapidly iterate and improve upon existing techniques. This rigorous evaluation isn’t merely academic; it translates directly into more reliable AI systems capable of tackling complex financial challenges. Consequently, businesses and individuals alike stand to benefit from enhanced accuracy in forecasting, risk assessment, and resource allocation, ultimately leading to more informed financial strategies and greater overall efficiency in the marketplace.

The Financial Quantitative Computation task challenges models to accurately answer user queries by retrieving and applying relevant <span class="katex-eq" data-katex-display="false">\mathbb{R}\mathbb{N}</span> numerical data and formulas from provided lists, demanding both financial domain expertise and high computational precision due to the potential for irrelevant information and large datasets. — The Financial Quantitative Computation task challenges models to accurately answer user queries by retrieving and applying relevant $\mathbb{R}\mathbb{N}$ numerical data and formulas from provided lists, demanding both financial domain expertise and high computational precision due to the potential for irrelevant information and large datasets.

The development of BizFinBench.v2 exemplifies the inevitable evolution of evaluative systems. Just as architectures inevitably age, so too must benchmarks adapt to increasingly sophisticated Large Language Models. The benchmark’s dual-mode approach-evaluating both core business capabilities and online performance-recognizes that true financial reasoning isn’t static; it’s a dynamic interplay of foundational knowledge and practical application. This mirrors the transient nature of improvements, rapidly outpacing comprehension. As Alan Turing observed, “Sometimes people who are unhappy tend to look at the world as if there were something wrong with it.” BizFinBench.v2 doesn’t assume the world is flawed, but rather acknowledges the need for continuous refinement in how LLMs are assessed against real-world financial challenges.

What Lies Ahead?

BizFinBench.v2, as a structured attempt to quantify financial reasoning in large language models, does not solve the inherent problem of evaluation-it reframes it. Every failure within the benchmark is a signal from time, a demonstration of the limits of current architectures when confronted with the subtle decay of real-world business data. The dual-track approach, probing both core capability and online performance, acknowledges the critical distinction between potential and manifestation-a useful, if belated, consideration.

Future iterations should not focus solely on expanding the dataset, but on incorporating dynamic elements-simulations of market shifts, evolving regulatory landscapes, and the inevitable introduction of noise. A static benchmark, however comprehensive, offers only a snapshot. True alignment with ‘expert-level financial capability’ demands an assessment of adaptability, of a system’s ability to learn from-and even anticipate-the erosion of established patterns.

Refactoring this benchmark, then, is not merely a technical exercise. It is a dialogue with the past, a continual recalibration of expectations. The ultimate metric will not be a score, but the rate at which the model’s performance degrades under conditions of increasing uncertainty-a measure of its resilience against the inevitable currents of time.

Original article: https://arxiv.org/pdf/2601.06401.pdf

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