When evaluating loan applicants, credit officers, insurance underwriters and financial professionals have the complicated task of accounting for all the factors in the applicant\u2019s profile that could lead to a default. Instead of relying on gut instinct, @RISK can support better risk modeling for finance.\u00a0 In this article, we\u2019ll show how to gain deeper insights into credit risks.<\/p>\n
Deterministic vs. Probabilistic Models<\/span> <\/strong><\/p>\n Most traditional spreadsheet-based risk models are deterministic: they rely on fixed assumptions about the business or income of the person applying for the loan. Testing assumptions about cash flow and other factors in a deterministic model would require making manual changes \u2013 a tall order when evaluating a complex organization.<\/p>\n @RISK, which runs as an add-on in Microsoft Excel, uses Monte Carlo simulation, a probabilistic analysis technique that accounts for uncertainty to help test assumptions more efficiently. With @RISK, users can create different types of models and analyses for nearly every risk assessment situation, including project planning and Six Sigma tolerances as well as approval decisions. The next two sections showcase two examples of financial risk models for credit officers and underwriters.<\/p>\n Financial Risk Model 1: Stationary Uncertainty<\/span> <\/strong><\/p>\n The first model evaluates a business applying for a $140,000 loan. It adds one basic layer of uncertainty by running simulations to determine the probability of the business experiencing a capital cost overrun. It also assumes that price will be uncertain, but based on a basic triangular distribution. There are no additional variables like inflation or changes in demand or supply.<\/p>\n Running the model simulates different cash flow scenarios for the business. If the business draws down its working capital and then overruns it by 20%, the model will flag that as bankruptcy. After running the model, it\u2019s also possible to use @RISK to quickly generate trend charts and summaries, including a prediction of the maximum solvency loan. After running this model 5,000 times, the determination was that the business stayed solvent 87% of the time, making it a good candidate for a loan.<\/p>\n![\"\"](\"https:\/\/sumood.sa\/wp-content\/uploads\/2025\/07\/Example-of-cash-flow-model-in-@RISK.png\")
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