In business, uncertainty is the only constant. Yet despite its critical impact, financial strategies are still held back by outdated tools—namely, deterministic models in Microsoft Excel. These static spreadsheets oversimplify future scenarios and ignore the real-world variability that defines business today.
But how can you make better decisions when your tools aren’t built for uncertainty?
Fortunately, there’s a powerful way to upgrade financial Excel models: @RISK. This Excel add-in converts static models into a dynamic version that handles Big Data efficiently, reduces modeling cycle-time while reducing bias and errors, and delivers valuable insights into the critical metrics driving decisions.
Created in partnership with industry expert Lachlan Hughson, founder of 4-D Resources Advisory LLC, “The New Corporate Finance Playbook” shows you practical strategies for applying probabilistic thinking to core areas of corporate finance—how to build more realistic forecasts, evaluate M&A scenarios under uncertainty, improve capital raising decisions with better risk-adjusted insights, and strengthen overall risk and sensitivity analysis.
What’s inside?
Static vs. probabilistic models
Deterministic models—those built with Excel alone—only allow one fixed input per cell and produce a single output. While simple, they fail to reflect the complexity and variability of real-world business dynamics.
In contrast, probabilistic models use dynamic inputs and account for uncertainty. By running thousands of simulations with Monte Carlo analysis in @RISK, you can explore a full spectrum of possible futures, understand their likelihoods, and make better-informed decisions.
Wherever variability exists—M&A valuations, debt covenant risks, equity returns—dynamic financial modeling gives Boards, executives, and investors a smarter foundation for strategic planning.
Real-world applications of financial modeling
Mergers and acquisitions
When undertaking a merger, the exchange ratio is a critical metric to determine if the agreed price is fair. However, traditional analyses often rely on “rules of thumb” and “guesstimates” for model inputs, making the outputs less objective.
Since Excel alone can't fully capture the variability and relationships among all inputs, how can companies calculate a truly objective exchange ratio range?
@RISK solves this by allowing users to incorporate the variability of all inputs—no matter how many—and define their interrelationships. This generates a full output range from a single model, eliminating the need for multiple two-input sensitivity analyses.
A relative frequency graph can then compare the median transaction value to the agreed transaction value, providing a far more objective basis for evaluation.
Capital raising
When raising debt capital, companies face the risk of default on principal and interest payments. Understanding the probability of default—or breaching a debt covenant— is critical to determining the appropriate capital structure.
Traditional Excel models can’t provide the necessary insight into the range of possible credit metric outcomes. @RISK solves this by modeling the variability and relationships of all relevant inputs, generating an objective range to assess the likelihood of covenant breaches.
For example, @RISK can simulate the annual range of the Total Debt/EBITDA ratio, helping companies better understand their probability of default and make smarter debt decisions.
Get the full financial modeling playbook
For corporate finance professionals seeking to make smarter, value-driving decisions, turn the finance function into a competitive advantage, and future-proof their careers, this eBook shows how @RISK makes all three goals possible—right now.