Insurance premium renewal propensity

Objective: Premium paid by the customer is the major revenue source for insurance companies. Default in premium payments results in significant revenue losses and hence insurance companies would like to know upfront which type of customers would default premium payments.

The objective of this project is to predict the probability that a customer will default the premium payment, so that the insurance agent can proactively reach out to the policy holder to follow up for the payment of premium.

About the dataset

The dataset contains the following information about 79854 policy holders:

1.id: Unique customer ID

2.perc_premium_paid_by_cash_credit: What % of the premium was paid by cash payments?

3.age_in_days: age of the customer in days

4.Income: Income of the customer

5.Marital Status: Married/Unmarried, Married (1), unmarried (0)

6.Veh_owned: Number of vehicles owned (1-3)

7.Count_3-6_months_late: # of times premium was paid 3-6 months late

8.Count_6-12_months_late: # of times premium was paid 6-12 months late

9.Count_more_than_12_months_late: # of times premium was paid more than 12 months late

10.Risk_score:Risk score of customer

11.No_of_dep: Number of dependents in the family on the customer (1-4)

12.Accomodation: Owned (1), Rented (0)

13.no_of_premiums_paid: # of premiums paid till date

14.sourcing_channel: channel through which customer was sourced

15.residence_area_type: Residence type of the customer

16.premium renewal: Y variable - 0 indicates that customer has not renewed the premium and 1 indicates that customer has renewed the premium:

1. Data split: Training & Test

2. Modelling Process (validation & interpretation)

3. Naive-Bayes Model