Chapter 2: Data to Insights to Decisions

Question 1

What are the potential analytics solutions to motor insurance fraud?

Answer 1

-Claim prediction
- Member prediction
-Application prediction
-Payment prediction

Question 2

What are the key questions required to convert a business problem into an analytics solution?

Answer 2

-What is the business problem? (org not always sure, should be expressed in business terms and have nothing to do with analytics)
-What are the goals the business wants to achieve?
-How does the business currently work? (situational fluency, understand the business well enough to converse with partners)
-In what ways could a predictive data analytics model help to address the business problem? (explore possibilities)

Question 3

What are the questions involved in evaluating the feasibility of a proposed analytics solution?

Answer 3

-Is the data required by the solution available or could it be made available?
-What is the capacity of the business to utilize the insights the analytics solution will provide?

Question 4

What will claim prediction do?

Answer 4

A model could be built to predict the likelihood that an insurance
claim is fraudulent

Question 5

What will member prediction do?

Answer 5

A model could be built to predict the propensity of a member to commit fraud in the near future

Question 6

What will application prediction do?

Answer 6

A model could be built to predict, at the point of application,
the likelihood that a policy someone has applied for will ultimately result in a
fraudulent claim

Question 7

What will payment prediction do?

Answer 7

A model could be built to predict the amount most likely to be paid
out by an insurance company after having investigated a claim