Modeling can also be a good option when an organization is grappling with massive complexity. In a large company with many different types of businesses, even if you have data on each line of business, the task of assembling and using that data to better understand risk may be virtually impossible. For large, complex systems, risk modeling may offer a more direct path to the insights needed to make smarter decisions.
Risk modeling shouldn’t be considered a replacement for risk analytics. View it instead as another tool in the analytical arsenal – one that is best used when you need to make more informed decisions on forward-looking issues of strategic importance, but don’t have traditional data sources to draw from. Here are some samples of modeling that can be used.
Credit-risk modeling is intended to aid financial institutions in quantifying, aggregating and managing risk across geographical and product lines. The outputs of these models also play increasingly important roles the risk management and performance measurement processes for banks and insurance companies, in particular.
The outputs of credit-risk models include performance-based compensation, customer profitability analysis, risk-based pricing and, to a lesser (but growing) degree, active portfolio management and capital-structure decisions.
Asset and portfolio modeling is used to manage and streamline the organization’s balance sheets by enhancing profitability while taking into account such conflicting objectives as reducing capital, minimizing risk and increasing liquidity.
Stress-testing is “a risk-management tool used to evaluate the potential impact on portfolio values of unlikely, although plausible, events or movements in a set of financial variables,” according to the Federal Reserve Bank of San Francisco.
Liquidity risk modeling analyzes the liquidity issues within an organization’s balance sheets, both under normal and under stress situation.
Cash flow at risk, which measures possible shortfalls in cash flow helps to analyze a company’s commodity-purchasing exposures.
It is important to keep in mind that when a company analyzes a potential project, it is forecasting potential, not actual, cash flows for a project. Forecasts, of course, are based on assumptions that may be incorrect. Company should thus perform sensitivity analyses on its assumptions to get a better sense of its estimations of risk on the project the company is about to take.
There are three risk-analysis techniques that many organizations use to analyze their risks to a potential project:
Net-present-value analysis is a form of sensitivity analysis that can help a company gauge how sensitive its NPV analysis is to changes in its assumptions of variables. To begin the analysis, one must first come up with a base-case scenario. This is typically the NPV using assumptions one believes are most accurate. From there, one can change various assumptions that were first made based on other potential assumptions.