Prediksi Klaim Asuransi Perjalanan Menggunakan Machine Learning untuk Optimasi Manajemen Risiko

Andy Hermawan; Nila Rusiardi Jayanti; Adam Praharsya Rahmadian; Muhammad Hafizh Bayhaqi; Amira Afdhal; Kerin Aurelia

doi:10.59841/saber.v3i2.2476

Authors

Andy Hermawan Universitas Indraprasta PGRI
Nila Rusiardi Jayanti Universitas Indraprasta PGRI
Adam Praharsya Rahmadian Purwadhika Digital Technology School
Muhammad Hafizh Bayhaqi Purwadhika Digital Technology School
Amira Afdhal Purwadhika Digital Technology School
Kerin Aurelia Purwadhika Digital Technology School

DOI:

https://doi.org/10.59841/saber.v3i2.2476

Keywords:

classification, insurance claim prediction, machine learning, travel insurance

Abstract

Travel insurance provides financial protection for individuals during their trips, both domestically and internationally. With the increasing demand for travel insurance, insurance companies face challenges in efficiently managing claims. This study aims to develop a predictive model to classify whether an insurance policy will be claimed based on historical customer and transaction data. This research utilizes a dataset containing various features related to travel and policyholders, such as agent type, distribution channel, insurance product, travel duration, and premium amount. The methods used include data exploration, feature processing, and the application of machine learning algorithms such as Logistic Regression, Random Forest, and XGBoost. Experimental results indicate that the XGBoost model performs the best, achieving the highest accuracy compared to other models. With this predictive model, insurance companies can optimize claim evaluation processes, reduce fraud risks, and improve operational efficiency in handling travel insurance claims.

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