Year: 2024
Type: article
Abstract: As the volume of insurance transactions continues to increase, combating insurance fraud has become increasingly important. One of the key challenges is how to predict fraud based on high-dimensional data. In the current research landscape, deep learning methods that perform well often require large amounts of data, but their training and deployment also pose significant computational challenges. This paper, based on the Alibaba Tianchi dataset, explores the feasibility of constructing a low-cost prediction system through feature engineering and parameter tuning. Additionally, this paper verifies the robustness of the eXtreme Gradient Boosting (XGBoost) model in handling high-dimensional sparse features and imbalanced data.
DOI: 10.54097/rd1m7n64
Author: Mengda Lu
Institution: Jiangnan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405808871