Predictive modeling is a mathematical technique that uses historical data and results to create a model that can predict future outcomes. For example, banks use predictive modeling in credit scoring to foresee a potential client’s ability to repay a loan. Airlines can also use the technique to predict the volume of passengers for a particular month or season.
Some may argue that predictive modeling rests on the premise that history repeats itself, which is not too far-fetched. After all, financial predictive models can predict if a client is likely to make late payments in the future based on his or her past behavior.
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While there are several predictive modeling approaches, artificial intelligence (AI), machine learning (ML), and data science play a significant role in developing predictive models. Large corporations that use big data (i.e., billions of rows of data) can develop predictive models that identify trends and predict demands for specific products based on the given information.
However, predictive modeling can also be as simple as using Excel to predict the weight a person can lose given his or her adherence to a diet and exercise. The video below shows how to do just that using multiple linear regression, a predictive modeling approach.
General Process to Create a Predictive Model
Regardless of the approach to predictive modeling used, creating a predictive model follows a general process that involves:
- Data cleansing: First, you need to clean the data of outliers or data points significantly different from all the others.
- Predictive modeling approach selection: Decide on a predictive model approach. You can choose from regression analysis, linear approach, neural networks, and other predictive modeling techniques.
- Data processing: The data needs to be processed into a consumable format suitable for the chosen predictive modeling technique.
- Data subset selection and training: A subset of the data should be selected to train the model.
- Predictive model testing: Testing the model is necessary to ensure that it is accurate. At this point, the model should be used on independent data for further validation.
Actual Applications of Predictive Modeling
Predictive modeling can be seen in action in various industries and varying functions. A simple example in predicting weight loss using Excel has been provided earlier, but here are more ways people are using predictive modeling:
Reduce Aircraft Maintenance Costs
Airlines use predictive modeling to detect and predict aircraft parts’ aging, allowing them to schedule aircraft maintenance and correctly order the necessary parts. They do this by recording the pieces’ conditions using sensors, which alert them in case they need to repair or replace parts.
Financial Management Assistance
Several banks developed applications that use predictive modeling to help clients better manage their finances. These applications can remind clients of upcoming lease or loan payments and even warn them of overspending.
Credit Card Fraud Detection and Prevention
Banks and credit card companies use data from the client’s past account usage and employ predictive modeling to detect anomalies. For instance, a credit card that has always been used within the U.S. is suddenly used in Mexico. Financial predictive models would alert the credit card owner since his or her account may have been compromised.
Improve Retail Customer Experience
Retail companies use transactional data to track the best days to schedule sales promotions, which products need promotion, and where to position certain products, to name a few. Walmart, for one, has a patent that allows them to track shoppers’ journeys down grocery aisles. Data from this technology enables Walmart to categorize shoppers and predict their movements and purchases.
Companies are using predictive modeling to streamline business processes, protect clients, and improve products and services. ML and AI have made the technique more powerful.