Stochastic modeling is a form of financial model that is used to help make investment decisions. This type of modeling forecasts the probability of various outcomes under different conditions, using random variables. Stochastic modeling presents data and predicts outcomes that account for certain levels of unpredictability or randomness. Companies in many industries can employ stochastic modeling to improve their business practices and increase profitability. In the financial services sector, planners, analysts, and portfolio managers use stochastic modeling to manage their assets and liabilities and optimize their portfolios. Deterministic modeling produces constant results Deterministic modeling gives you the same exact results for a particular set of inputs, no matter how many times you re-calculate the model. Here, the mathematical properties are known. None of them is random, and there is only one set of specific values and only one answer or solution to a problem. With a deterministic model, the uncertain factors are external to the model. Stochastic modeling produces changeable results Stochastic modeling, on the other hand, is inherently random, and the uncertain factors are built into the model. The model produces many answers, estimations, and outcomes—like adding variables to a complex math problem—to see their different effects on the solution. The same process is then repeated many times under various scenarios. Stochastic modeling is used in a variety of industries around the world. The insurance industry, for example, relies heavily on stochastic modeling to predict how company balance sheets will look at a given point in the future. Other sectors, industries, and disciplines that depend on stochastic modeling include stock investing, statistics, linguistics, biology, and quantum physics. A stochastic model incorporates random variables to produce many different outcomes under diverse conditions.