What Is Predictive Modeling?

Predictive modeling is the process of using known results to create, process, and validate a model that can be used to forecast future outcomes. It is a tool used in predictive analytics, a data mining technique that attempts to answer the question "what might possibly happen in the future?"

Understanding Predictive Modeling

The rapid migration to digital products has created a sea of data that is easily available and accessible for businesses. Big data is utilized by companies to improve the dynamics of the customer-to-business relationship. This vast amount of real-time data is gotten from sources like social media, internet browsing history, cell phone data, and cloud computing platforms.

By analyzing historical events, there is a probability that a business might be able to predict what would happen in the future and plan accordingly. However, this data is usually unstructured and too complex for humans to analyze in a short period of time. Due to the complexity that enormous amounts of data present, companies are increasingly using predictive analytics tools to forecast the outcome of an event likely to happen in the near future.

How Predictive Analytics Works

Predictive analytics collects and processes historical data in huge amounts and uses powerful computers to assess what happened in the past, and then provides an assessment of what will happen in the future.

Predictive analytics uses predictors or known features to create predictive models that will be used in obtaining an output. A predictive model is able to learn how different points of data connect with each other. Two of the most widely used predictive modeling techniques are regression and neural networks.

Companies are increasingly using predictive modeling to make predictions about events likely to happen in the near future.

Special Considerations

In the field of statistics, regression refers to a linear relationship between the input and output variables. A predictive model with a linear function requires one predictor or feature in order to predict the output/outcome. For example, a bank that hopes to detect money laundering in its early stages might incorporate a linear predictive model.

The bank specifically wants to know which of its customers are likely to engage in money laundering activities at some point in time. All the bank’s customers’ data are presented, and a predictive model is built around the dollar value of transfers each customer made during a period of time.

The model is taught to recognize the difference between a money laundering transaction and a normal transaction. The optimal outcome from the model should be a pattern that signals which customer laundered money and which didn’t. If the model perceives that a pattern of fraud is emerging for a particular customer, it will create a signal for action which will be attended to by the bank’s fraud analysts.

Predictive models are also used in neural networks such as machine learning and deep learning, which are fields in artificial intelligence (AI). The neural networks are inspired by the human brain and are created with a web of interconnected nodes in hierarchical levels which represents the foundation for AI. The power of neural networks lies in their ability to handle non-linear data relationships. They are able to create relationships and patterns between variables that would prove impossible or too time-consuming for human analysts.

Key Takeaways

  • Predictive modeling is the process of using known results to create, process, and validate a model that can be used to make future predictions.
  • Two of the most widely used predictive modeling techniques are regression and neural networks.

So while a bank can input known variables such as the value of transfers initiated by its customers into its model in order to obtain the desired outcome of who is likely to engage in money laundering, a neural network can create a more powerful pattern if it can successfully create a relationship between input variables like time logged in, geographic location of the user, IP address of the user’s device, recipient or sender of the funds, and any other feature that is likely to make up a laundering activity.

Other predictive modeling techniques used by financial companies include decision trees, time series data mining, and Bayesian analysis. Companies that take advantage of big data through predictive modeling measures are better able to understand how their customers engage with their products and can identify potential risks and opportunities for a company.