A wide variety of techniques in statistics which range from machine learning, modeling, and mining of data is put into use in the wider field of predictive analytics. They are used to predict unknown and future events.
In the case of businesses, predictive models make the most of the patterns found to exist in historical data and those present during transactions in order to pick out opportunities as well as risks. Models make a record of the relationship between various factors which facilitate how risks are assessed and the potential which accompany particular condition sets and guide the process of making decisions with regards to transactions by candidates.
Predictive analytics provides a probability or predictive score for all individuals like employees, customers, patients, products, components, vehicles, machines or other units that form the basis of particular organizations. This is done to specify, inform and influence processes of organizations regarding individuals who are large in number like the assessment of credit risks, marketing, manufacturing, detection of fraud, healthcare, and operations of governments including enforcement of law.
The use of predictive analysis is widely prevalent in the fields of marketing, actuarial science, insurance, financial services, retail, telecom, healthcare, travel, pharmaceuticals amongst various other fields.
The best-known example of predictive analysis is perhaps credit scoring which is put into use across the breadth of financial services. These models take into account the credit history, data and loan applications of customers so that individuals may be ordered into ranks on the basis of their likelihood to make timely credit payments in the future.
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