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Fraud Detection in Vehicle Insurance Claims Using Machine Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Fraud Detection in Vehicle Insurance Claims Using Machine Learning./
Author:	Zhang, Ziyang.
Description:	1 online resource (48 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9798382772134

Fraud Detection in Vehicle Insurance Claims Using Machine Learning.
Zhang, Ziyang.

Fraud Detection in Vehicle Insurance Claims Using Machine Learning. - 1 online resource (48 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--University of California, Los Angeles, 2024.

Includes bibliographical references

Insurance fraud poses a significant financial burden on the industry, with fraudulent vehicle insurance claims being a major contributor. This study explores the application of machine learning techniques to accurately detect fraudulent vehicle insurance claims. Six different models - Logistic Regression, Random Forest, Gaussian Naive Bayes, Decision Tree, XGBoost, and Gradient Boosting classifiers - are evaluated on an imbalanced dataset. To address class imbalance, oversampling techniques like SMOTE, Borderline SMOTE, and ADASYN are employed. Performance is assessed using metrics such as F1 score, recall, and AUC. Results indicate that XGBoost and Gradient Boosting models demonstrate superior overall performance, effectively balancing precision and recall. The Gaussian Naive Bayes model exhibits exceptional recall, making it suitable for minimizing missed fraud cases.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382772134Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

Fraud detectionIndex Terms--Genre/Form:

554714
Electronic books.

Fraud Detection in Vehicle Insurance Claims Using Machine Learning.
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502 $a Thesis (M.S.)--University of California, Los Angeles, 2024.
504 $a Includes bibliographical references
520 $a Insurance fraud poses a significant financial burden on the industry, with fraudulent vehicle insurance claims being a major contributor. This study explores the application of machine learning techniques to accurately detect fraudulent vehicle insurance claims. Six different models - Logistic Regression, Random Forest, Gaussian Naive Bayes, Decision Tree, XGBoost, and Gradient Boosting classifiers - are evaluated on an imbalanced dataset. To address class imbalance, oversampling techniques like SMOTE, Borderline SMOTE, and ADASYN are employed. Performance is assessed using metrics such as F1 score, recall, and AUC. Results indicate that XGBoost and Gradient Boosting models demonstrate superior overall performance, effectively balancing precision and recall. The Gaussian Naive Bayes model exhibits exceptional recall, making it suitable for minimizing missed fraud cases.
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653 $a Fraud detection
653 $a Vehicle insurance
653 $a Machine learning
653 $a Logistic regression
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