From the course: Financial Modeling and Forecasting Financial Statements

Use AI to revise financial statement forecasts

From the course: Financial Modeling and Forecasting Financial Statements

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Use AI to revise financial statement forecasts

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Sophisticated Wall Street analysts update their net income forecast continually in the days and hours before a company publicly announces its actual net income. These up-to-date estimates aid analysts in determining whether the earnings actually announced were better or worse than what would have been expected given the prevailing attitudes about the company's economic situation. This type of real-time interpretation of publicly released earnings news is not possible with stale data. And AI models can keep the estimates fresh by constantly updating them as information in the online environment changes. AI models do this by being connected to live data feeds. For a financial analyst outside a company, these live data feeds are market prices, inflation and interest rate data, the text of press releases and industry commentary, and social media trends. For an analyst working inside a company, these live data feeds include all of the external information plus everything coming from the company's internal information system, real-time sales data, cash collections and payments, new contracts signed by salespeople, and so forth. In short, AI models increase the speed and scope of everything. The speed is increased because the AI models are tapped directly into the live data sources. There's no need for a human being to first see the data and then enter it into the system. And the scope is increased because AI models can scan the entire information environment, not just a few variables from a few sources. And by using past data to calibrate how reliable and accurate different data sources are, AI models can properly put extra weight on data sources that have been accurate in the past. Here is what ChatGPT says about the power of real-time forecast revision. Instead of waiting for quarter-end closes, CFOs and managers get a real-time financial crystal ball where every new invoice, sales order, or macroeconomic report nudges the forecast into sharper focus. Now let's highlight financial forecasting and budgeting inside a company. An important use of internal managerial accounting information is in controlling a company's operations through tracking actual performance and comparing that performance to what was planned. So how can artificial intelligence improve a company's ability to use internal managerial accounting information in controlling company operations? Well as an example, in a traditional managerial accounting system, variance analysis, that's comparing budgeted results to actual results, has often been done on a monthly or a quarterly basis, which unfortunately may give indications of fixable problems after it's too late to to fix them. With AI, variance analysis can be done in real time, allowing managers to act immediately to fix a problem before it can cause much damage. And AI models can take the information from the variances and feed it into the next round of budgets, making those budgets better. Through this iterative process, AI models can continuously improve both the budgeting process and the operational control processes that compare these budgets to the actual real-time results.

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