Auditing Finance & Banking prompts in 中文
Writing finance & banking prompts in 中文 is a different discipline from writing English prompts and translating the output. Simplified vs Traditional matters — state it explicitly. Qwen 3 Max leads on Mandarin; GPT-5 and Claude are close behind. Avoid mixing English technical terms unless they're industry-standard. The auditor on this page scores your prompt against four 2026 frontier models — GPT-5, Claude 4.6, Gemini 3 Ultra and DeepSeek V3.2 — and ranks them by V-Index (quality per dollar) for DCF models, pitch decks, credit memos, and IPO prospectuses.
Finance & Banking work is unforgiving of model error. A hallucinated citation, a missed performance obligation, a wrong incoterm — each one costs hours or money downstream. The cheapest model is rarely the most expensive; the model that hallucinates least on your specific workload is. Our V-Index methodology measures both, in 中文, against the actual prompt you intend to ship.
Four pillars of a high-V-Index finance & banking prompt
- Pillar 1
Comparable set definition
Define your comps universe explicitly — region, size band, vertical. 'Find me comps for a fintech Series B' returns garbage; 'public fintech infrastructure cos with $200M-1B ARR in EMEA' returns a defensible set.
- Pillar 2
Assumption transparency
Force the model to list every assumption with a unit. 'WACC = 9.2% (4% Rf + 1.2 beta × 4.3% ERP)' is auditable. 'About 9%' is not.
- Pillar 3
Sensitivity, not point estimates
Always ask for a low/base/high case. Single-number outputs from LLMs are false precision.
- Pillar 4
Source attribution
Require URL + date for every number. Stale data is worse than missing data in a credit memo.
Five mistakes that tank finance & banking prompt quality
- 01Asking for a valuation without stating the methodology — DCF vs multiples produces different numbers.
- 02Single-number outputs instead of sensitivity ranges.
- 03No comp set definition — model picks irrelevant comparables.
- 04Vague assumption language ('roughly', 'around') — un-auditable.
- 05Trusting model-generated tickers and CUSIPs without verification.
Three example finance & banking prompts to audit
Each version below progressively adds the constraints discussed above. Run them through the auditor and watch the V-Index move.
Draft an investment committee memo for a $80M Series B in a Gulf fintech.
Draft an investment committee memo for a $80M Series B in a Gulf fintech, and end with a one-sentence summary for the partner.
Draft an investment committee memo for a $80M Series B in a Gulf fintech. List every assumption explicitly. Refuse to answer any sub-question you cannot support with a cited source.
Frequently asked questions
- Which model is best for finance & banking prompts in 中文?
- There is no universal answer — it depends on whether you optimise for cost, quality, or hallucination rate on your specific workload. The auditor on this page ranks GPT-5, Claude 4.6, Gemini 3 Ultra and DeepSeek V3.2 by V-Index for your exact prompt in 中文. As a rule of thumb in 2026: Claude 4.6 leads on finance & banking reasoning tasks, DeepSeek V3.2 wins on cost-per-quality, GPT-5 is the safest all-rounder.
- Does prompt language affect output quality?
- Yes — significantly. Prompts in 中文 route to different attention patterns than English prompts, even when the underlying request is identical. Simplified vs Traditional matters — state it explicitly. Qwen 3 Max leads on Mandarin; GPT-5 and Claude are close behind. Avoid mixing English technical terms unless they're industry-standard. For high-stakes finance & banking work, audit in both languages and compare.
- Is the free tier enough for finance & banking work?
- The free tier (3 anonymous audits + 5/day signed-in) is enough to validate a prompt template you'll reuse. For daily finance & banking work — refining client-specific prompts, generating PDF audit reports, switching between 中文 and Professional English — Pro at $99/year removes the limits.
- How is V-Index calculated?
- V-Index = curated quality score (1–10) ÷ input price per 1M tokens (USD). A higher V-Index means more quality per dollar. The quality score is task-weighted: a model that is excellent at reasoning but weak at extraction will score differently for a finance & banking extraction prompt than for a finance & banking reasoning prompt.
- Are model citations reliable?
- No. Every frontier model in 2026 still fabricates citations at a non-zero rate, including the most expensive ones. The mitigation is in the prompt: require the model to refuse rather than guess, and verify every citation manually before shipping. Our audit reports flag citation-heavy prompts with an explicit hallucination-risk score.