Case Study: Income Prediction in the Insurance Sector

Background:

A leading insurance company has been struggling to accurately predict the income of their potential customers. They have been relying on traditional methods such as credit scores and occupation, but have noticed that these metrics are not always reliable indicators of income. The company has a large dataset of customer information, including personal and professional details. They have approached a data science consulting firm to help them build a machine learning model that can predict income more accurately.

Problem Statement:

The insurance company wants to build a model that predicts the income of potential customers. They want to use this model to target their marketing campaigns towards customers who are likely to have a higher income, and therefore, be more likely to purchase their premium products.

Data:

The data provided by the insurance company includes personal information such as age, gender, marital status, and education level. It also includes professional information such as occupation, job title, and industry. In addition to this, the dataset includes other variables such as credit score, loan status, and location.

Numerical Variables:

• Age
• Income
• Education level
• Credit score
• Number of dependents
• Home value
• Net worth
• Debt-to-income ratio

Categorical Variables:

• Gender
• GMarital status
• GOccupation
• GRace/ethnicity
• GLocation (e.g. zip code or city)
• GEducation level (e.g. high school diploma, bachelor's degree, etc.)
• GEmployment status (e.g. full-time, part-time, self-employed, unemployed)
• GType of insurance policy (e.g. health, life, auto)

Methodology:

The data science consulting firm started the project by exploring the data to understand the relationships between different variables and income. They found that there were several variables that had a strong correlation with income, such as education level, occupation, and credit score.

The firm decided to use a machine learning algorithm called XGBoost to predict income. They preprocessed the data by filling in missing values, converting categorical variables to numerical, and scaling the data. They split the data into training and testing sets, with 80% of the data used for training and 20% used for testing.

They trained the model on the training set and used the testing set to evaluate the performance of the model. The evaluation metrics used were mean absolute error (MAE) and root mean squared error (RMSE).

Results:

The XGBoost model was able to predict income with a MAE of $8,720 and RMSE of $11,215. These values were significantly lower than the baseline model, which had a MAE of $12,340 and RMSE of $14,810. The firm also used feature importance techniques to identify the most important variables in predicting income, which were education level, occupation, credit score, and age.

Conclusion:

The XGBoost model developed by the data science consulting firm was able to predict income more accurately than the traditional methods used by the insurance company. The model can be used to target marketing campaigns towards customers who are likely to have a higher income, increasing the chances of selling premium products. The insurance company can also use the insights gained from the feature importance analysis to make more informed decisions in their business strategy.