Excel의 금융 모델링을 위한 AI

A three-statement model links the income statement, balance sheet and cash flow statement, so a change in one assumption, such as revenue growth, flows through all three. Net income feeds retained earnings on the balance sheet and is also the first line of the cash flow statement. Changes in working capital, capital spending and debt explain the change in cash, and ending cash lands back on the balance sheet. If everything is wired correctly, assets equal liabilities plus equity in every period. A DCF model then takes projected free cash flow and discounts it at a rate such as the weighted average cost of capital (WACC). It adds a terminal value to cover the years after the forecast ends. AI helps in several places. Microsoft 365 Copilot in Excel and add-ins from other AI vendors can write formulas from a description, suggest a model layout, explain an inherited formula and summarize what drives a result. General chat assistants can walk through the logic of a DCF or suggest a sensible set of assumptions to test. Python in Excel lets users run code for simulations and charts inside the workbook. The danger is silent error: a model that calculates without any warning but gives the wrong answer. Common problems in AI-built models include numbers typed over formulas, ranges that stop one row short, relative references that shift when copied, units mixed between thousands and millions, and cash outflows with the wrong sign. Another is a balance sheet forced to balance with an unexplained plug number. A common misconception is that a model that balances is correct. Balancing proves only that the model is internally consistent. Wrong assumptions or a mistaken discount period can still produce a balanced, confidently wrong valuation. Another misconception is that AI output needs less review than a junior analyst's work. It needs the same line-by-line review.