BizFinBench.v2: Real-World Online Benchmark for Expert Finance AI
Table of Contents
Introduction
If you’ve been tracking how artificial intelligence is shaping finance, you’ve probably run into a problem: most benchmarks measure how well a model answers questions in neat, offline chunks, not how it performs in the messy, fast-moving world of money. BizFinBench.v2 is a bold step toward closing that gap. Built on authentic data from both Chinese and U.S. equity markets, this benchmark combines offline tasks with online, real-time testing. In other words, it’s designed to answer a simple question: can today’s LLMs actually help with real-money financial decisions as they unfold online?
This is not just an incremental tweak to evaluation metrics. BizFinBench.v2 represents a shift toward business-grounded assessment, where the authors cluster actual user queries from financial platforms into eight core tasks across four business scenarios and add two online tasks that hinge on live market data. The result is a large-scale, bilingual benchmark—29,578 expert-level QA pairs—that aims to mirror the practical demands financial professionals face. If you’re curious about the nuts and bolts, the paper is available in full at the arXiv link: https://arxiv.org/abs/2601.06401. The authors note that the data and code are open for researchers via the BizFinBench.v2 repository (https://github.com/HiThink-Research/BizFinBench.v2).
Now, let’s unpack what makes BizFinBench.v2 distinctive, what it reveals about current AI capabilities in finance, and how practitioners can put this into action.
Why This Matters
The financial sector lives in a world that’s not content with “one-and-done” answers. Market conditions shift by the minute; risk management hinges on timely interpretation of streams of data; and the appetite for automated, real-time decision support is growing across banks, asset managers, and fintechs. BizFinBench.v2 tackles this head-on by foregrounding two realities that many prior benchmarks missed: authenticity and online capability.
Why it’s significant right now: Banks and brokers are actively seeking AI that not only reasons about static facts but also remains robust when confronted with live data, noisy inputs, and evolving market narratives. BizFinBench.v2’s dual-track design—core business capabilities plus online performance—maps directly onto the day-to-day workflow of financial institutions, where you need steady knowledge and real-time action in parallel.
A real-world scenario where it’s applicable today: Imagine a portfolio risk desk using an LLM to triage client inquiries (the offline tasks) while simultaneously receiving intraday price feeds and news alerts (the online tasks). The system should not only explain a company’s earnings history but also offer timely, executable insights that respect trading costs, latency, and risk constraints. BizFinBench.v2’s SPP (Stock Price Prediction) and PAA (Portfolio Asset Allocation) online tasks are designed to stress-test exactly those capabilities in a controlled, reproducible way.
How this builds on prior AI finance work: Earlier benchmarks tended to be static, schematic, or synthetic—good for gauging basic language understanding or numerical reasoning in isolation, but poor at reflecting how models fare in real markets with live data, long contexts, and multi-faceted business logic. BizFinBench.v2 augments offline evaluation with online evaluation, and it anchors both tracks in authentic market data and real user queries. That combination is what makes the benchmark a more meaningful predictor of operational performance, not just academic prowess.
Main Content Sections
Authentic Data and Dual-Track Evaluation
BizFinBench.v2 isn’t playing with toy datasets. It is anchored in authentic business data drawn from both Chinese and U.S. equity markets and organized around four core business scenarios:
Business Information Provenance (BIP): The frontline, interaction-heavy zone where a model handles direct user inquiries amid real-world noise—typos, irrelevant snippets, and data perturbations. Tasks here include Anomaly Information Tracing (AIT), Financial Multi-turn Perception (FMP), and Financial Data Description (FDD).
Financial Logical Reasoning (FLR): This is where models must reason with market data and interfaces to reach defensible judgments. It includes Financial Quantitative Computation (FQC), Event Logical Reasoning (ELR), and Counterfactual Inference (CI).
Stakeholder Feature Perception (SFP): Focused on summarizing and interpreting market signals for stakeholders. Tasks include User Sentiment Analysis (SA) and Financial Report Analysis (FRA, limited to the Chinese market).
Real-time Market Discernment (RMD): The online track, where data and decisions happen in real time. The two online tasks are Stock Price Prediction (SPP) and Portfolio Asset Allocation (PAA). Real-time, public market data drives these tasks, and the evaluation uses a simulated but realistic trading environment for PAA.
Altogether, BizFinBench.v2 comprises 29,578 Q&A pairs. The design process involved clustering millions of user queries from authentic platforms, followed by a rigorous three-tier quality-control pipeline to ensure authenticity, usefulness, and compliance. The offline tasks rely on a three-level screening workflow: platform clustering and desensitization, frontline staff review, and expert cross-validation. In online tasks, a panel of senior financial experts defined structured data and prompts to mirror genuine market environments, while keeping sensitive data secure.
From a practical standpoint, this means you’re not just testing a system’s ability to spit out correct words. You’re evaluating whether an AI can navigate real-world business logic, interpret long conversational histories, and make data-informed decisions in a live market setting. For teams building AI-enabled financial services, this is the kind of benchmark that aligns model testing with business outcomes.
For more granular numbers and data breakdowns (per task, token counts, and task distribution), the paper offers a detailed appendix. A quick note: offline tasks include AIT, FMP, FDD, FQC, ELR, CI, SA, FRA with the listed counts; online tasks include SPP and PAA. The authors also provide a robust discussion of how input lengths vary by task, which is crucial when choosing models with finite context windows.
If you want to dive into the technicalities or reuse the dataset for your own experiments, you’ll find the data and code referenced in the paper, and the authors explicitly invite open-source use in the ongoing research cycle.
Online vs Offline Tasks and Bilingual Scope
A standout feature of BizFinBench.v2 is its bilingual, dual-market scope. It tests models on data and queries from both China’s and the United States’ equity ecosystems, which introduces language, regulatory, and market structure considerations you simply don’t encounter in single-market benchmarks. That bilingual dimension is more than a novelty; it exposes how models handle cross-market semantics, different accounting standards, and diverse investor behavior.
On the online side, the two tasks push models to operate with live data and real trading constraints. SPP asks for daily closing-price predictions on watchlist stocks by integrating historical prices, real-time ticks, technical indicators, and news. PAA is even more ambitious: an investment-simulation system that enforces real-world trading rules, including transaction costs, latency, and slippage. Decisions occur hourly, with an end-to-end process from analysis to order submission—essentially a microcosm of a live fund’s decision loop.
To maintain integrity, the online tasks rely on actual market configurations and prompts, with the authors planning to open-source the LLM investment system they used. This transparency allows other researchers to test different models against a consistent, market-grounded setup, which is a big leap toward reproducibility in finance AI research.
In terms of metrics, the authors use a mix: main tasks are verifiable open-ended questions evaluated primarily on accuracy (with conformal prediction techniques used for sentiment analysis and stock-price predictions to reflect business tolerance levels). The online asset allocation task is evaluated with business metrics like cumulative return, Sharpe ratio, and maximum drawdown, which align with practical investor risk-reward objectives rather than pure scoring. This blend ensures the benchmark speaks both the language of language models and the language of finance.
If you’d like to skim the numbers and distributions, the paper’s Appendix Tables summarize the per-task question counts, average input token counts, and the distribution across tasks. It’s worth noting that the total question count includes 4,000 for AIT, ELR, SA, and CI; 2,000 for FQC and FRA; 3,741 for FMP; 3,837 for FDD; 4,000 for SPP; and PAA as an online task where the number of questions can scale with the market, not fixed in the same way as offline tasks.
What We Learn About FinAI Capabilities
The results in BizFinBench.v2 are telling, not in a victory lap sense but in a diagnostic way. The full study evaluated 21 LLMs, including leading proprietary models like ChatGPT-5, Gemini-3, Claude-Sonnet-4, Grok-4, Doubao-Seed-1.6, and Kimi-k2, along with open-source contenders such as Qwen variants, InternLM, GLM-Z, and DeepSeek variants. Four domain-focused financial models (Fin-R1, Dianjin-R1, FinX1, Fino1) were also part of the mix.
Overall performance: ChatGPT-5 leads with an average accuracy of 61.5% across main tasks, signaling strong generalist capabilities in a finance-forward setup but still below expert human performance (the study places expert-level performance around the mid-to-high eighties in similar contexts). The upshot is that even top consumer-oriented LLMs struggle to reach human expert parity in real-world financial tasks, particularly in the more demanding online scenarios.
Open-source vs proprietary dynamics: Among open-source models, Qwen3-235B-A22B-Thinking-2507 tops the open-source pack with about 53.3% average accuracy. Among domain-specialist models, Dianjin-R1 is the top performer in its own class but still trails the best overall by a sizable margin. The takeaway is nuanced: scale and specialized fine-tuning help, but domain alignment with real-world, online financial tasks remains a bottleneck.
Task-specific patterns: Larger parameter counts tend to help with high-precision tasks such as FDD (Financial Data Description), FQC (Financial Quantitative Computation), and CI (Counterfactual Inference). However, even strong models struggle with high-precision numerical reasoning and long-context integration, underscoring a chronic gap in data-grounded analytics and multi-step financial reasoning.
Sentiment and online agility: The sentiment analysis task (SA) proves especially tough for LLMs, with the top model achieving only around 23.5% accuracy—far below human performance. This highlights how subjective judgments, nuanced tone, and investor psychology still elude current AI systems. The stock-price prediction task (SPP) reflects the same theme: the best model reaches only 36.9% accuracy, indicating that precise, data-driven market movement forecasting remains a hard problem.
Portfolio Asset Allocation (PAA) insights: In this online, practical task, DeepSeek-R1 shines on real-world metrics like total return and Sharpe ratio while keeping drawdown within a reasonable band. In contrast, some top consumer models performed poorly in actual investment performance, reminding us that excellent language play doesn’t automatically translate into robust trading strategies.
Expert-informed error analysis: BizFinBench.v2 includes a business-oriented error taxonomy, identifying five recurring dilemmas: Financial Semantic Deviation, Long-term Business Logic Discontinuity, Multivariate Integrated Analysis Deviation (MIAD), High-precision Computational Distortion, and Financial Time-Series Logical Disorder. The distribution of errors is similar across many models, but some—like ChatGPT, Gemini-3, Doubao-Seed-1.6, and Qwen3-235B-A22B-Thinking—struggle most with MIAD, pointing to gaps in information synthesis and cross-variable reasoning. Grok-4 and Claude-Sonnet-4 show relatively stronger performance in high-precision computations but still lag behind the front-runners in integrated finance tasks.
If you want the full picture, the authors provide detailed examples and appendices that illustrate these error modes and their practical implications for model tuning. The key takeaway is that BizFinBench.v2 isn’t just a scorecard; it’s a failure-mode map that highlights where modern LLMs consistently stumble in real financial work.
For a sense of how these findings translate to real-world deployment, the authors also discuss that in offline tasks, actual business privacy constraints limit disclosure of detailed rubrics, whereas online tasks are designed to be open and reproducible. This dual stance acknowledges the tension between data privacy and transparency in finance AI research.
The benchmark’s error analysis isn’t just diagnostic; it’s actionable. The paper outlines targeted optimization directions—from enriching training data with richer, longer financial narratives to improving scenario adaptability and long-context reasoning—so that developers can move from “good enough” to “operationally reliable” in financial settings.
If you’d like to read more about the experimental setup, figures, and in-depth results, the main paper and its Appendix D (error examples) are a goldmine for researchers aiming to bridge the gap between benchmark performance and real-world finance applications. And yes, the benchmark’s data and prompts for online tasks are meant to be openly shared to encourage broader experimentation.
Practical Implications for Deployment
So what does BizFinBench.v2 mean for practitioners who actually build and deploy AI in financial services?
A better qualifier for model selection: Instead of relying solely on offline Q&A accuracy, you should consider online performance metrics and long-context reasoning capabilities. BizFinBench.v2 shows that a model can be strong in general questions but stumble when real-time market dynamics and execution constraints come into play.
A blueprint for targeted optimization: The expert-driven error taxonomy translates into concrete optimization goals—improve cross-variant data integration for MIAD, sharpen numerical precision for FQC, and strengthen time-series consistency to reduce Financial Time-Series Logical Disorder. If you’re fine-tuning an LLM for trading or risk, these are the levers to pull.
A path toward responsible, real-world AI: The benchmark includes online data configurations that mirror authentic market conditions, and it emphasizes compliance and privacy in offline tasks. For financial institutions, BizFinBench.v2 offers a framework for validating AI capabilities under realistic governance and risk constraints before any broad deployment.
Open-ended experimentation with reproducible environments: The planned open-sourcing of the online investment system and the data/code repository means you can reproduce results, test alternate models, and explore scenario extensions. This is a valuable resource for teams benchmarking internal AI pilots against a common standard.
A bilingual, cross-market perspective: With data drawn from both Chinese and U.S. markets, BizFinBench.v2 nudges AI developers to consider cross-market semantics and regulatory contexts. If your product aims to serve global clients or operate in multiple jurisdictions, this dual-market lens matters.
In short, BizFinBench.v2 isn’t just a paper; it’s a playbook for evaluating and sharpening AI in a field where real-time, business-critical decisions are the norm. It also signals where the field still has work to do—especially in translating online, data-heavy performance into reliable, profit-enhancing actions.
The authors also point out limitations and future directions, such as broadening data coverage to capture niche query types, expanding online scenarios beyond stock price and asset allocation, and introducing Few-Shot evaluation paradigms in addition to Zero-Shot and Chain-of-Thought prompts. This honesty about boundaries is valuable for researchers who want to build on the framework rather than replicate it verbatim.
For a deeper dive into how BizFinBench.v2 is constructed, how tasks map to business workflows, and the exact experimental setup, the original paper offers a thorough road map. If you’re curious about practical next steps—how to adapt BizFinBench.v2 to your organization’s data and workflow—you can start by inspecting the paper’s Appendix A (for task architecture) and Appendix B (for experimental settings), then explore the online repository for data and code.
Key Takeaways
BizFinBench.v2 is the first large-scale financial benchmark anchored in authentic business data from both Chinese and U.S. equity markets, with online evaluation integrated into the same framework.
The benchmark covers eight offline tasks across four core business scenarios (BIP, FLR, SFP, RMD) and two online tasks (SPP, PAA), totaling 29,578 expert-level QA pairs.
In head-to-head testing of 21 LLMs, ChatGPT-5 achieved the top overall accuracy (61.5%), but expert performance still significantly outpaces current models, especially for tasks demanding long context, nuanced financial logic, or precise numerical computation.
Online tasks reveal a more challenging landscape: even strong models struggle with stock price prediction and asset-allocation decisions in real-time market conditions, highlighting a gap between benchmark performance and practical deployment.
The error taxonomy—Financial Semantic Deviation, Long-term Business Logic Discontinuity, MIAD, High-precision Computational Distortion, and Financial Time-Series Logical Disorder—provides a concrete guide for where to focus future improvements.
Beyond scoring, BizFinBench.v2 offers a business-centric lens on LLM capabilities, enabling a structured route to improve AI systems for real-world finance use cases.
The dataset and code are open for research, with plans to open-source the online investment system to encourage broad experimentation and comparison across models.
If you’re building AI for finance, BizFinBench.v2 gives you a rigorous, business-grounded yardstick—one that expects your model to do more than talk; it expects it to think and act in real, time-sensitive financial environments. For a deeper read and full context, check out the original paper at https://arxiv.org/abs/2601.06401 and explore the repository for practical implementation details.
Sources & Further Reading
Original Research Paper: BizFinBench.v2: A Unified Dual-Mode Bilingual Benchmark for Expert-Level Financial Capability Alignment
Authors: Xin Guo, Rongjunchen Zhang, Guilong Lu, Xuntao Guo, Shuai Jia, Zhi Yang, Liwen Zhang
