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https://www.um.edu.mt/library/oar/handle/123456789/126581
Title: | Rooting out deception : the application of tree-based learners for motor insurance fraud detection |
Authors: | Grima, Lorin (2023) |
Keywords: | Fraud -- Spain Decision trees Insurance -- Spain Fraud investigation -- Spain |
Issue Date: | 2023 |
Citation: | Grima, L. (2023). Rooting out deception: the application of tree-based learners for motor insurance fraud detection (Bachelor's dissertation). |
Abstract: | This dissertation investigates motor insurance fraud detection in the Spanish market by implementing and comparing tree-based methods, renowned for their performance and interpretability. The study begins with basic Decision Trees and progresses to tree-based ensemble methods, including Random Forests, Gradient Boosting machines, and Newton-based boosting techniques such as LightGBM, XGBoost, and CatBoost. A significant challenge in motor insurance fraud detection is addressing the class imbalance. To address this issue, the dissertation evaluates cost-sensitive learning approaches and resampling techniques to optimize model performance. The analysis concludes that a cost-sensitive LightGBM model is the most effective method for this scenario, achieving a balanced accuracy of 81% and successfully identifying 83% of fraudulent cases. The findings of this study provide valuable insights into the effectiveness of tree-based methods in detecting motor insurance fraud and highlight the potential of LightGBM in efficiently identifying fraudulent cases. By presenting a rigorous comparison of different techniques and addressing the class imbalance issue, this research contributes to the ongoing development of interpretable solutions for combating insurance fraud. |
Description: | B.Sc.(Melit.) |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/126581 |
Appears in Collections: | Dissertations - FacSci - 2023 Dissertations - FacSciSOR - 2023 |
Files in This Item:
File | Description | Size | Format | |
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2308SCISOR320105065426_1.PDF Restricted Access | 7.38 MB | Adobe PDF | View/Open Request a copy |
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