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, telecommunication, retail, travel, healthcare, pharmaceutical 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.
Collective Changes Ethical Application of Predictive Analytics
Collective Changes is dedicated to the ethical use of any and all predictive analytics derived from the data collected in serving the mentors and mentees in the process that they are engaged with us. We feel that there is a unique data set that will provide the sustainability factor for Collective Changes to allow us to mentor the number of people we would like to reach. This data can be very valuable as it will provide an insight into the future habits of a new and emerging middle class of consumers in developing countries.
As corporations look for ways to grow new markets, as their markets in developed countries mature, the predictive analytics will allow them valuable insights for making long lead time manufacturing and distribution decisions in areas of the world that have yet to be served. The middle class is the engine that drove the US to become the world market that it is and the new emerging middle class in developing countries can do the same for those countries.
We must manage the information carefully and at all times protect the personal information of the mentors and mentees. Any distribution of data will be disconnected from personal information.
Additionally, Collective Changes will screen organizations based on their ethical commitment. A good example of this is data will not be provided to tobacco companies.