In predictive analytics, what does a scoring model do?

A scoring model in predictive analytics plays a crucial role in decision-making by assigning numerical scores to data points based on historical data patterns. This process involves using various algorithms and statistical techniques to analyze past behavior or outcomes and then generate scores that represent the likelihood of future events or behaviors.

For example, in a credit scoring model, historical data regarding individual borrowers' repayment behaviors is used to create a model that can predict the creditworthiness of new loan applicants. The model assigns scores based on similarities found in the historical data, effectively evaluating how likely an applicant is to repay a loan.

This is significant because it provides organizations with a quantifiable and objective basis for making decisions, such as approving loans, targeting marketing efforts, or identifying potential risks. By leveraging historical data, scoring models enhance predictive accuracy and help businesses optimize their strategies for better results.