Car Insurance Policies. TEAM 1 Vijayakumar Ayyaswamy Logan Baranowitz Cyrus Havewala Stephanie Romich

Transcription

1 Car Insurance Policies TEAM 1 Vijayakumar Ayyaswamy Logan Baranowitz Cyrus Havewala Stephanie Romich

2 Agenda Data Source Description Key Question Data Preprocessing and Exploration Models Summary and Conclusion

3 Data Source Insurance data from a car insurance firm in Holland Contains 9,822 records and 85 dimensions Each record represents a residential zip code Dimensions include many demographic variables, average number of different policies and average contribution amounts for each policy.

4 Key Question What characteristics distinguish certain zip codes to purchase at least one car insurance policy? Car Policy Count % of Total 0 4,825 49% 1 4,997 51% Grand Total 9, % MISSION: Explanatory SUCCESS VARIABLE: Car Policy = 1

5 Data Preprocessing Translate the dataset Many bins in the set Convert some ranges to approximate averages Interpret ranges Ranges shown appear to grow exponentially Demographic Ranges Contribution Ranges Average ln(average) 0 0% 0 f % 1 f % 2 f % 3 f % 4 f % 5 f % 6 f % 7 f % 8 f % 9 f ?

6 Data Exploration Demographic data did not show clear relationship to Car Policy Home Ownership Religion Education

7 Data Exploration (cont d) Contribution amounts of individual policies showed variances

8 Data Exploration (cont d) When looking at average total policies in each zip code, large differences occurred for those with more than 2 total policies.

9 Data Models Naïve Rule Baseline comparison to test the model Majority is with purchasing at least one policy Base error rate is 49.12% Purchase at least one car policy Total Percentage 0 4, % 1 4, % Total 9, %

10 Data Models (cont d) Classification Tree Used the log of all individual contribution amounts, total policies > 2 variable and <50% have no car variable Tree was pruned to use only 6 decision points Additional contribution variables had very little effect on the overall accuracy. Error rate is 34.51%

11 Data Models (cont d) rdParty_prv MopedPolicy_ FirePolicy_c FirePolicy_c BicyclePolic Policy # >

12 Data Models (cont d) Logistic Regression Started with same variables as the Classification Tree (including all contributions) Narrowed best output to a model with nine variables Error rate is 35.93% Interesting note four of the contribution variables had negative coefficients, meaning that zip codes with higher average contributions to these policies were less likely to purchase at least one car insurance policy

13 Data Models (cont d) Prior class probabilities According to relative occurrences in training data Class 1 0 Prob <-- Success Class The Regression Model Input variables Constant term Contribution_3rdParty_prvt_T Contribution_3rdParty_firms_ Contribution_tractorPolicy_Tr Contribution_MopedPolicy_Tr Contribution_FirePolicy_Tran Contribution_BicyclePolicy_T Contribution_ss_ins_policy_t Policy Count > 2 (not car) <50% No Car Coefficient Std. Error p-value Odds * Training Data scoring - Summary Report Cut off Prob.Val. for Success (Updatable) 0.5 Classification Confusion Matrix Predicted Class Actual Class Error Report Class # Cases # Errors % Error Overall

14 Model Summary Model Sensitivity Specificity False Positive False Negative Overall Error Lift Naïve Rule 50.88% 49.12% 0.00% 49.12% 49.12% - Classification tree 54.63% 45.37% 24.18% 10.33% 34.51% 29.74% Logistic Regression 64.07% 35.93% 27.65% 8.28% 35.93% 26.86% Classification Tree Model has the lowest overall error rate, Logistic Regression fits the goal better

15 Summary of Analysis High contributions to bicycle, moped, fire policies and third party firms will not contribute to car insurance - areas may be densely populated and preference is smaller vehicles. High contributions to social security insurance and tractor policies show potential for car insurance areas may be farm lands or outskirts where the need for car is more. Areas with more than 50% of population with at least one car and have at least 2 other types of policies are conducive for business.

16 Recommendations The Company can not look at demographics to understand current customers in Holland Print advertising and direct mail marketing: Advertise on local papers in the targeted zip codes and send out direct mails through postal. Joint marketing with car dealers in the area may prove profitable. Provide bundled products of car and tractor policies

17 Additional Observations Lots of data that pertains to a select group of zip codes, so more variables were needed to capture these nuances Demographic data was not much help and is difficult to interpret No cars was used as this appeared to have a preconceived significance to having car insurance. Dataset with all categorical variables created additional challenges.

18 Questions?