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?"
- 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.
- Companies can use predictive modeling to forecast events, customer behavior, as well as financial, economic, and market risks.
Understanding Predictive Modeling
By analyzing historical events, companies can use predictive modeling to increase the probability of forecasting events, customer behavior, as well as financial, economic, and market risks.
The rapid migration to digital products has created a sea of data that is readily available to 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 retrieved from sources such as social media, internet browsing history, cell phone data, and cloud computing platforms.
However, the data is usually unstructured and too complex for humans to analyze in a short period of time. Due to the sheer volume of data, companies use predictive modeling tools–often via computer software programs. The programs process huge amounts of historical data to assess and identify patterns within the data. From there, the model can provide a historical record as well as an assessment of what behaviors or events are likely to occur again or in the future.
Predictive modeling can be used by sports teams to analyze the probabilities of success using player statistics and situational analysis.
Applications of Predictive Modeling
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.
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 or outcome. For example, a bank that hopes to detect money laundering in its early stages might incorporate a linear predictive model.
The bank wants to identify which of its customers are likely to engage in money laundering activities at some point in time. Using the bank’s customer data, a predictive model is built around the dollar amount of money transfers that customers 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 prevention unit.
Predictive Modeling Tools
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.
On the one hand, a bank can input known variables, such as the value of transfers initiated by its customers into its model to determine who is likely to engage in money laundering. On the other hand, a neural network can create a more powerful pattern by creating a relationship between input variables. These input variables could include time logged in, geographic location of the user, IP address of the user’s device, recipient or sender of the funds, and any other variable or behavior that is likely to be involved in money 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 the company.