Predictive analytics encompasses a variety of techniques from statistics, modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.
Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields.
One of the most well known applications is Credit Scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. A well-known example is the FICO score.
The growth of Predictive Analytics is also driving the need for Business Modelling and Decision Modelling. If you can't model a business or a problem in its current state, its unlikely that you will be able to model it into the future.
Predictive Analytics also highlights the issue of accountability for decisions, and that is the driving force for Business Modelling.
Types of Predictive Analytics
- Supervised Methods
- Require training and data
- UnSupervised Methods
- Doesn't require training. Uses existing rules or methods (eg. clustering) to determine a result.
- Analytical Techniques