This project aims to enhance the accuracy and efficiency of travel insurance claim processing using Machine Learning. Insurance companies often face high volumes of claims influenced by various factors, which can slow down decision-making and increase the risk of errors. Meanwhile, customers expect a fast and reliable claims process.
Leverage ML models to predict the likelihood of a claim, enabling:
-
⚡ Faster claim decisions
-
✅ Reduced human error
-
📈 Improved operational efficiency
-
Model performance evaluated using ROC Curve
-
Applied hyperparameter tuning to optimize model accuracy
-
Balanced focus on precision and recall to minimize misclassification
Implementing ML in this workflow helps:
-
Speed up simple claim approvals
-
Allow human adjusters to focus on complex cases
-
Increase customer satisfaction through responsive and accurate service --
Clarissa Beatrice Kosasih
Jeremy Djohar Riyadi
Kelvin Jonathan Yusach
Sherly Vaneza