Financial Modeling prompts for Financial Analyst

You are a financial modeler. Purpose of the model: [purpose]. Business: [business model]. Time horizon and granularity: [horizon & granularity]. Data I have: [data I have]. Audience: [audience]. Lay out the structure: the tabs and their purpose, the driver-based logic (what drives revenue, what drives cost, and in what order), where assumptions live and how they are flagged, and the outputs that answer the decision. List every assumption the model requires, marked as (a) grounded in my historicals, (b) benchmarkable externally, or (c) a guess. For the guesses, note which ones the output is most sensitive to — those deserve a scenario, not a point estimate. Do not invent benchmark figures.

Act as a skeptical CFO reviewing a model. Assumptions: <assumptions> [assumptions] </assumptions> Outputs: [outputs]. Business context: [context]. For each assumption, ask: what is the evidence, is it consistent with our own history, does it implicitly assume something else (a hiring plan, a market share, a conversion rate) that we have not stated, and what happens to the output if it is 30% worse? Identify the two or three assumptions the answer actually hinges on and build a sensitivity table around them. Then name the assumption most likely to be wrong in the same direction as our hopes. Give a downside case that is plausible rather than theatrical, and say what it would mean for [key decision].

You are a model reviewer. Here is my model structure and formulas: <model> [model] </model> Intended behavior: [intended behavior]. Check for the errors that actually break models: hardcoded numbers inside formulas, inconsistent formulas across a row, circular references, sign errors, mismatched periods, ranges that do not extend to new rows, double-counting between sheets, and balance sheet items that will not tie. For each issue: location, what goes wrong, and the corrected formula. Then give me three checks to build into the model itself — a balance check, a total tie-out, and a plausibility flag — so the model catches its own errors next time. Note what you cannot verify without the workbook.