BizFinBench.v2: A Unified Dual-Mode Bilingual Benchmark for Expert-Level Financial Capability Alignment

🍞 Hook: Imagine your school gives a math test with only easy, made-up questions. You might get a great score, but does that mean you can solve real homework problems? Probably not. That’s how many finance AI tests used to be.

🥬 The Concept: Benchmarks are tests for AIs; in finance, many were based on simple or synthetic (made-up) data and only checked offline skills.

• How it works (before):
  1. Collect generic questions or simulate finance data
  2. Ask models to answer in a quiet, offline setting
  3. Score by accuracy on short, fixed tasks
• Why it matters: Without real, messy market conditions and live data, we don’t know if an AI can actually help investors, advisors, or risk teams when the market moves.

🍞 Anchor: It’s like training for a soccer game only by reading a rulebook—then being surprised you struggle during a fast, rainy match.

🍞 Hook: You know how the weather changes every hour and your outfit choices need to keep up? Finance is like that—prices move quickly and news breaks suddenly.

🥬 The Concept: Real-time assessment means evaluating AI while the market is moving, not just on frozen, past data.

• How it works:
  1. Stream real market prices, news, and events
  2. Ask the AI to make decisions (like buy/sell) repeatedly
  3. Measure gains, risk, and consistency over time
• Why it matters: If a model only works on old, tidy data, it may fail in live trading or risk alerts.

🍞 Anchor: A coach doesn’t judge players just on drills; they watch them in a real game with real pressure.

🍞 Hook: Think of two classrooms: one speaks Chinese and one speaks English. If your AI can only help one room, half the students miss out.

🥬 The Concept: Bilingual benchmarking checks that AIs handle both Chinese and English business contexts.

• How it works:
  1. Use real data from China and the U.S. equity markets
  2. Build tasks in both languages
  3. Score consistently across languages
• Why it matters: Finance is global; cross-lingual capability avoids one-sided skills.

🍞 Anchor: A travel guide who speaks both languages can help tourists on both sides of the ocean.

🍞 Hook: You know how customer questions at a help desk can be long, messy, and sometimes off-topic? Real investor questions are the same.

🥬 The Concept: Authentic business data means the questions and numbers come from actual financial platforms, not toy examples.

• How it works:
  1. Cluster huge piles of real user queries
  2. Desensitize private info
  3. Have frontline staff and senior experts clean and validate items
• Why it matters: Models trained or tested on pretend data often freeze when faced with typos, mixed signals, or conflicting reports.

🍞 Anchor: It’s like practicing cooking with real groceries from a busy market, not with plastic toy food.

🍞 Hook: Imagine older tests checked only spelling, not storytelling. In finance, older benchmarks often checked narrow skills.

🥬 The Concept: The gap was that evaluations ignored live scenarios and complex business logic, so scores didn’t match real performance.

• How it works:
  1. Offline only → no pressure, no latency, no slippage
  2. Synthetic tasks → miss real workflows and odd edge cases
  3. Single-skill focus → overlook teamwork of skills (reasoning + math + memory)
• Why it matters: A high benchmark score that doesn’t predict real outcomes leads to bad decisions in live operations.

🍞 Anchor: A student who aces flashcards might still struggle to write a full essay under time pressure.

🍞 Hook: Imagine a driving test that includes both a simulator AND a real road test in traffic. You’d trust that license more.

🥬 The Concept: The key insight is to align AI evaluation with real business finance by combining authentic data with both offline core skills and live online performance in one bilingual benchmark.

• How it works:
  1. Gather real Chinese and U.S. market queries and data
  2. Build four scenarios with eight offline tasks and two online tasks
  3. Evaluate accuracy offline and risk-adjusted returns online
  4. Analyze errors through a financial expert lens
• Why it matters: This reveals what AIs can truly do for investors, advisors, and institutions when markets move.

🍞 Anchor: It’s like testing a chef by grading their recipe knowledge and also tasting their food during a busy dinner rush.

Multiple analogies:

• Sports: Practice drills (offline tasks) + real match (online tasks). A player strong in drills might freeze under pressure; this benchmark checks both.
• Cooking: Reading recipes (offline reasoning) + cooking for real customers with time and cost constraints (online asset allocation).
• Classroom: Worksheets (offline Q&A) + class debate with surprise questions (online decisions on live data).

🍞 Hook: You know how a puzzle gets easier when you see the picture on the box?

🥬 The Concept: Dual-track evaluation means you test two lanes—core skills and live performance—so you see the whole picture.

• How it works:
  1. Lane 1 (Core Business Capabilities): reasoning, math, reports, multi-turn memory
  2. Lane 2 (Online Performance): stock prediction intervals and portfolio allocation under real rules
  3. Compare models across both lanes
• Why it matters: A model great at one lane but weak in the other can still fail in production.

🍞 Anchor: A student who aces quizzes but panics during presentations needs different training than one who presents well but misses facts.

🍞 Hook: Picture sorting a huge bin of mixed LEGO pieces into neat sets before building.

🥬 The Concept: Business-level deconstruction breaks finance AI ability into clear tasks that mirror real workflows.

• How it works:
  1. Cluster real user questions
  2. Map them to four scenarios: provenance, reasoning, stakeholder perception, and real-time discernment
  3. Design ten tasks that stress specific skills
• Why it matters: Clear parts show where each model struggles and how to fix it.

🍞 Anchor: If the bike chain keeps slipping, you know to fix the chain—not the wheels.

🍞 Hook: Think of a bilingual referee who knows the rules in two leagues.

🥬 The Concept: Bilingual coverage ensures the same high bar in Chinese and English markets.

• How it works:
  1. Real data and tasks from both markets
  2. Consistent scoring and expert review
  3. Cross-lingual comparability
• Why it matters: Finance insights often cross borders; evaluation should, too.

🍞 Anchor: A global weather report is better than one that only covers one city.

At a high level: Real user queries and market data → Task construction and quality control → Offline accuracy and online portfolio metrics → Model rankings and expert error analysis.

🍞 Hook: Imagine turning a messy backpack into labeled folders so you can actually find your homework.

🥬 The Concept: Clustering groups similar real user finance questions so tasks mirror actual needs.

• How it works:
  1. Algorithmically cluster platform queries
  2. Label by business scenario and task type
  3. Keep real-world noise (typos, irrelevant bits) to test robustness
• Why it matters: Without realistic variety, models pass easy tests but fail real users.

🍞 Anchor: Grouping math problems by topic helps you practice what you’ll really see on the test.

🍞 Hook: You know how doctors cover patient names on shared reports? Finance data needs privacy, too.

🥬 The Concept: Desensitization removes personal and sensitive corporate info while keeping the business logic intact.

• How it works:
  1. Strip PII and sensitive tags
  2. Keep factual market signals and structures
  3. Re-check compliance
• Why it matters: Realism without risking privacy or breaking rules.

🍞 Anchor: It’s like masking a house number but leaving the street map so directions still work.

Three-level quality control recipe:

1. Platform clustering and desensitization; 2) Frontline staff review (remove duplicates, invalids; keep real quirks); 3) Senior experts cross-validate for accuracy and business alignment.

Four scenarios and ten tasks (each kept brief):

• 🍞 Hook: Think of being a detective finding the real cause of a market jump. 🥬 AIT (Anomaly Information Tracing): Find the core event behind a stock’s odd move by filtering noisy, mixed sources.

  • How: Collect clues → filter distractions → link to price move
  • Why: Misreading catalysts leads to wrong advice. 🍞 Anchor: Pinpointing that a surprise earnings beat—not a rumor—caused the spike.

• 🍞 Hook: Like chatting with a friend over many messages. 🥬 FMP (Financial Multi-turn Perception): Answer follow-ups using long chat history and prior answers.

  • How: Track context → update memory → stay consistent
  • Why: Forgetting context gives flip-flop answers. 🍞 Anchor: Remembering the user holds 200 shares before suggesting risk steps.

• 🍞 Hook: Spot-the-mistake puzzles are fun—and important. 🥬 FDD (Financial Data Description): Judge if described numbers and logic match the real data.

  • How: Cross-check figures → flag inconsistencies → conclude
  • Why: Incorrect summaries mislead decisions. 🍞 Anchor: Catching a P/E ratio miscomputed from stale earnings.

• 🍞 Hook: Like doing precise cooking measurements. 🥬 FQC (Financial Quantitative Computation): Compute indicators from complex financial statements and formulas.

  • How: Pick the right formula → extract numbers → calculate carefully
  • Why: Tiny math errors snowball into big money mistakes. 🍞 Anchor: Correctly computing CAGR over the true time span.

• 🍞 Hook: Arranging story cards in the right order. 🥬 ELR (Event Logical Reasoning): Order events by time or cause-effect to predict impact chains.

  • How: Identify triggers → map propagation → infer outcomes
  • Why: Mixed-up order breaks reasoning. 🍞 Anchor: Policy change → input cost rise → margin squeeze → stock drop.

• 🍞 Hook: What-if games are great for planning. 🥬 CI (Counterfactual Inference): Reason under hypothetical policy or outcome changes.

  • How: Swap assumption → recompute paths → compare to baseline
  • Why: Scenario planning needs disciplined logic. 🍞 Anchor: What if tariffs were cut by 5% this quarter?

• 🍞 Hook: Reading room mood in class. 🥬 SA (User Sentiment Analysis): Score user investment sentiment from profiles, holdings, market context, and news.

  • How: Weigh signals → produce a score or interval
  • Why: Mistaking strategy for fear gives wrong guidance. 🍞 Anchor: Trend investor worried? Or just rotating sectors?

• 🍞 Hook: Comparing team stats across leagues. 🥬 FRA (Financial Report Analysis): Rank companies using full financials plus fundamentals/technicals/news (China-focused here).

  • How: Normalize metrics → integrate signals → rank
  • Why: Fair comparisons need holistic, consistent scoring. 🍞 Anchor: Ranking five EV makers on growth, margins, and leverage.

Online tasks:

• 🍞 Hook: Predicting today’s recess bell time with a plus/minus window. 🥬 SPP (Stock Price Prediction): Predict daily close with an interval that must contain the true price given a 1% tolerance.

  • How: Use recent prices, indicators, and news → output interval
  • Why: Point guesses are brittle; intervals match business tolerance. 🍞 Anchor: “$49.80–$50.30” counts as correct if the true close is $50.05.

• 🍞 Hook: Playing a video game where trades cost coins and the clock keeps ticking. 🥬 PAA (Portfolio Asset Allocation): Hourly decisions on real-time data with realistic frictions (fees, latency, slippage) and metrics (return, Sharpe, max drawdown).

  • How: Analyze signals → decide trade or skip → execute with costs → track PnL and risk
  • Why: Paper profits vanish if you ignore trading realities. 🍞 Anchor: A strategy with modest gains but tiny drawdowns can beat flashy, risky ones.

🍞 Hook: Different students need different test styles.

🥬 The Concept: Zero-shot vs Chain-of-Thought (CoT) evaluation checks robustness to prompting styles.

• How it works:
  1. Zero-shot: answer directly
  2. CoT: explain step by step, then answer
• Why it matters: Some models improve with reasoning steps; others get distracted.

🍞 Anchor: Some kids think better out loud; others quietly solve in their heads.

🍞 Hook: Like grading not just the final grade but also how steady the student performs.

🥬 The Concept: Risk metrics (Sharpe ratio and maximum drawdown) judge profit quality, not just size.

• How it works:
  1. Sharpe: return per unit of volatility
  2. Max drawdown: worst peak-to-trough loss
• Why it matters: Smooth, safer gains beat bumpy, scary rides at the same return.

🍞 Anchor: A calm A- every week is better than one A+ with several Ds.

Secret sauce:

• Real business data + bilingual coverage
• Dual-track design (offline depth + online realism)
• Interval-based grading for uncertainty-aware tasks
• Expert error analysis that pinpoints fixable weaknesses

🍞 Hook: Think of a school fair where each team plays puzzles (offline) and live challenges (online), and judges write exact reasons for each score.

🥬 The Concept: The authors tested 21 popular models plus two human financial experts across 10 tasks, using accuracy for offline and investment metrics for online.

• How it works:
  1. Offline: verifiable open-ended Q&A scored by accuracy; some tasks use prediction intervals with business tolerances (10% for sentiment, 1% for stock price)
  2. Online PAA: evaluate total return, Sharpe, and max drawdown under realistic trading rules
  3. Zero-shot as default; CoT also tested
• Why it matters: This mimics how firms would compare vendors and pick the right tool.

🍞 Anchor: It’s like comparing teams by quiz scores and also by how they perform in a time-pressured escape room.

The competition:

• Proprietary leaders (e.g., ChatGPT-5, Gemini-3, Doubao-Seed-1.6, Kimi-k2, Claude-Sonnet-4, Grok-4)
• Strong open-source families (Qwen, DeepSeek, GLM, InternLM)
• Finance-specialized models (Dianjin-R1, FinX1, Fino1, Fin-R1)
• Human financial experts as a gold-standard point of reference

Scoreboard with context:

• Offline main tasks: ChatGPT-5 averaged 61.5% accuracy (like a solid B when the class average is closer to a C), while the best open-source, Qwen3-235B-Thinking, got 53.3%.
• Domain-tuned models lagged: Dianjin-R1 scored ~35.7%, showing that training on synthetic or narrow financial data didn’t transfer to messy, real business tasks.
• Hard tasks stayed hard: User Sentiment Analysis topped out around 23.5% even for strong models; Stock Price Prediction best hit ~36.9% (interval-based), showing live market uncertainty is tough.
• Experts still rule: Financial experts reached about 84.8% offline, a big gap above all models.

Online portfolio allocation (PAA):

• DeepSeek-R1 led with +13.46% total return, profit factor 2.3, Sharpe 1.8, and a max drawdown of -8%—a balanced, risk-aware performance.
• Qwen3-Max and Grok-4 showed positive returns; Grok-4’s max drawdown was impressively small (~-1%), signaling strong risk control.
• ChatGPT-5 and Claude-Sonnet-4 underperformed the market baseline (SPY proxy), with negative or near-zero returns and Sharpe < 1, showing strategy tuning pain in live settings.

Surprising findings:

• Chain-of-Thought often hurt performance instead of helping; one model’s average fell dramatically under CoT, while DeepSeek-V3.2 improved, proving reasoning style is model-dependent.
• Error analysis exposed five repeated failure patterns: semantic drift, long-chain logic breaks, multivariate integration issues (MIAD), high-precision math mistakes, and time-series order confusion.

🍞 Hook: You know how a coach’s video review shows exactly where a team loses the ball?

🥬 The Concept: Expert-informed error analysis shows fixable weak spots.

• How it works:
  1. Sample 20% of wrong answers
  2. Classify them into five business-grounded error types
  3. Compare distributions by model
• Why it matters: Targeted fixes beat guesswork.

🍞 Anchor: If most turnovers come from bad passes, you drill passing—not shooting.

Limitations:

• Coverage bias: Clustering of platform queries might miss niche needs; more diverse sources could widen coverage.
• Online scope: Only two online tasks today (stock prediction and allocation); future versions can add alerts, recommendations, and compliance checks.
• Evaluation modes: Mostly zero-shot and CoT; few-shot and tool-augmented runs could change rankings.
• Transparency trade-offs: Offline rubric details can’t be released due to privacy/compliance; online configs and prompts will be open.

Required resources:

• Access to proprietary model APIs or strong open-source deployments (the paper used 8×NVIDIA H100s).
• Live or recent market data feeds for online tasks.
• Business experts to set tolerances, prompts, and realistic trading rules.

When not to use:

• Non-equity domains (e.g., credit underwriting or insurance claims) without adaptation.
• Ultra-low-latency trading where milliseconds matter; LLMs are not HFT engines.
• Settings that forbid any exposure to market data or that can’t support the compliance process.

Open questions:

• How much do few-shot examples and retrieval tools narrow the expert gap?
• Can fine-tuning on authentic, permissioned business logs fix MIAD and time-series logic issues?
• What safety layers best prevent hallucinations in high-stakes, long-context tasks?
• How to generalize from China/U.S. equities to bonds, derivatives, and global markets?
• What governance ensures privacy and fair use across bilingual, cross-border data?

Three-sentence summary:

• BizFinBench.v2 is a bilingual, dual-track benchmark built from authentic Chinese and U.S. equity market data that tests both offline core finance skills and online, real-time performance.
• It reveals a large expert gap: top models like ChatGPT-5 score well offline (61.5%) yet lag far behind human experts (84.8%), while DeepSeek-R1 leads live allocation with strong returns and risk control.
• Expert error analysis pinpoints five recurring business weaknesses, guiding targeted improvements.

Main achievement:

• Turning finance AI evaluation from synthetic, offline snapshots into a realistic, business-grounded, bilingual, and online-capable standard.

Future directions:

• Add more online tasks (alerts, recommendations, risk monitoring), broaden markets and asset classes, and explore few-shot/tool-augmented evaluations.
• Use the error taxonomy to drive data curation, reasoning improvements, and precision math modules.

Why remember this:

• It’s a trustworthy yardstick that connects benchmark scores to real finance outcomes, helping teams choose and improve models that actually deliver under market pressure.