Your client is an Insurance company and they need your help in building a model to predict the propensity to pay renewal premium and build an incentive plan for its agents to maximise the net revenue (i.e. renewals - incentives given to collect the renewals) collected from the policies post their issuance.
You have information about past transactions from the policy holders along with their demographics. The client has provided aggregated historical transactional data like number of premiums delayed by 3/ 6/ 12 months across all the products, number of premiums paid, customer sourcing channel and customer demographics like age, monthly income and area type.
In addition to the information above, the client has provided the following relationships: Expected effort in hours put in by an agent for incentives provided; and Expected increase in chances of renewal, given the effort from the agent.
Given the information, the client wants you to predict the propensity of renewal collection and create an incentive plan for agents (at policy level) to maximise the net revenues from these policies.
Find my code walkthrough in this jupyter notebook.
-- python3
Libraries
sklearn
numpy
matplotlib
seaborn
pandas