Traditional methods of assessment of risk for insurance would be time-consuming because they are more manual. Having this task automatic and scalable with machine learning models is helpful to companies to have an efficient system for risk evaluation. This project is supposed to analyze the data inputs which are demographic information, financial history, and health records to predict applicant risk levels. The insights derived will facilitate fairer premium pricing and speedier underwriting decisions.
Week 1-2: Data Collection and Preprocessing
Identify and clean data sources (personal, financial, and medical data).
Handle missing data and perform feature extraction.
Week 3-5: Feature Engineering and Model Development
Engineer meaningful features for risk prediction.
Train and evaluate machine learning models (e.g., decision trees, random forests).
Week 6-7: Model Evaluation and Optimization
Measure model accuracy, precision, recall, and other performance metrics.
Optimize hyperparameters and retrain for improved results.
Week 8-9: Tool Development
Develop the risk assessment interface (software or web app).
Integrate the machine learning model for real-time predictions.
Week 10-11: Testing and Validation
Perform extensive functional and non-functional testing.
Validate the system using new or unseen data.
Week 12: Final Delivery and Documentation
Finalize the tool for deployment.
Prepare documentation, including user guides and technical details.
