Smart Sell Re-quote project for an Insurance company.

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Daniel Lindsey
9 years ago
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1 SAS Analytics Day Smart Sell Re-quote project for an Insurance company. A project by Ajay Guyyala Naga Sudhir Lanka Narendra Babu Merla Kiran Reddy Samiullah Bramhanapalli Shaik

2 Business Situation XYZ is the leading Insurance company specializing in Auto Insurances. Re-quote project is an independent initiative by the company where the agent will be able to sell the unsold quotes back to the customers. Identifying the shared characteristics of agents and factors influencing them will help the company emerge as the leader in the market.

to sell the unsold quotes back to the customers.

3 Need for Prediction There is an imminent need to predict the characteristics of agents which will really influence the agents to use the re-quote project effectively. This information is necessary for the company to use the strategies/patterns and sell higher percentage of quotes. Discovery of different trends through this whole process will help better predict the percentage of re-quotes done.

This information is necessary for the company to use the strategies/patterns and sell higher

4 Data Preparation The modeling dataset has 42,322 observations and more than 300 variables. The data is messy, inconsistent, redundant and it also has many missing values. Some of the binary variables have more than 2 levels. Binary variables were recoded accordingly and were brought down to 2 levels. Measurement levels of certain variables needs to be recoded in order to obtain correct representation of the data. The missing values were imputed and the data was transformed to obtain normality using SAS Enterprise Miner.

Some of the binary variables have more than 2 levels. Binary variables were recoded accordingly and were brought down to 2 levels.

5 Sample dataset The data has a disproportionate distribution of the levels in target variable. The level HIGH has very less number of observations. We sampled out a dataset which has exactly 5320 observations of each level. This is done to have equal representation of the levels in the data which will in-turn enable us to build a good model.

We sampled out a dataset which has exactly 5320 observations of each level.

6 Prior Probabilities The random sampling dataset has a total of observations. This dataset is adjusted using prior probabilities to make it have all the original properties of the modeling dataset. Adjusted prior probabilities are entered in the exact same proportion as of the original modeling dataset.

This dataset is adjusted using prior probabilities to make it have all the

7 Model Building Different models like Autonomous Decision tree, Gini Tree, Entropy Tree, Probability tree, Forward Regression, Stepwise Regression, Polynomial Regression, Neural Network, Auto neural, Radial basis neural networks(equal and unequal width) are some of the models that we considered and built. More than 25 models were considered and analyzed for our project. Gini Decision tree was selected as the best model with an accuracy of 62.8% and sensitivity of 85.48%.

networks(equal and unequal width) are some of the models that we considered and built.

8 Overview of model statistics Model Misclassification Rate Average Square Error Sensitivity Specificity Autonomous Tree % 58.12% Probability Tree % 57.09% 3-way Split tree % 63.52% Gini Tree % 62.96% Entropy Tree % 69.35% Forward Regression % 65.51% Step Wise Regression % 65.64% Large P value Polynomial Regression % 65.86% Neural Network % 65.14% Auto Neural (Regression) % 61.62% Neural Network % 63.48% (Regression) Neural Network % 66.65% (6Nodes) RBF Unequal Width % 57.49% RBF Equal Width % 64.32% Ensemble % 62.77%

3781 0.1444 83.65% 65.64% Large P value 0.6666 0.2222 0 1 Polynomial Regression 0.3900 0.1504 82.48% 65.86% Neural Network 0.3854 0.1453 82.85% 65.14% Auto Neural (Regression) 0.3994 0.1555 84.68% 61.

9 Scoring We applied the learning from the modeling on the scoring data. From the scoring results it was found that 6.235% was predicted as the HIGH percentage of requotes done by agents.

10 Business Insights and Opportunities The most notable pattern in our predictions is that the agent who is active on FAO website would generate more revenue. The percentage change in the number of producers should be low over time as it would impact the requote rate. The number of sales representatives should be increased so as to target more number of agency locations. The effective buying income and population of an area are some key demographic factors influencing the percentage of re-quotes.

The percentage change in the number of producers should be low over time as it would impact the requote rate.

11 Business Recommendations The agents with VQAR comparative status should be encouraged. The agent who maintains special relationship with the company would add more credit in re-quote rate. The agent who is available on Find An Agent actively will be easily accessible to customers and should be encouraged. The company needs to focus on agents who handles lesser number of competitors. Establishing more number of ad campaigns might increase the probability of targeting large agencies and in-turn increasing the percentage of re-quotes.

The agent who is available on Find An Agent actively will be easily accessible to customers and should be encouraged.

12 For more details contact presenters at: Ajay Guyyala Phone: Naga Sudhir Lanka Phone: Narendra Babu Merla Phone: Samiullah Bramhanapalli Shaik Phone: Kiran Reddy Kondamadugula Phone: Faculty Advisor: Dr. Goutam Chakraborty

com Narendra Babu Merla Phone:862-576-2040 Email:narendra.itian@gmail.

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