Machine Learning based Method for Insurance Fraud Detection on Class Imbalance Datasets with Missing Values
DOI:
https://doi.org/10.63665/340bc990Keywords:
Insurance fraud detection, Machine learning, Class imbalance dataset, Missing values, Data preprocessing, Fraud classification, Precision, RecallAbstract
Machine Learning based insurance fraud detection plays an important role in identifying fraudulent insurance claims and reducing financial losses. In this project, machine learning techniques were implemented to detect fraudulent and genuine insurance claims on class imbalance datasets containing missing values. The dataset was preprocessed using data cleaning, missing value handling, normalization, and balancing techniques to improve model performance and reliability. Different machine learning algorithms were trained and evaluated to classify claims accurately despite the imbalance in data distribution.
The proposed system focuses on improving fraud detection accuracy while handling practical challenges such as incomplete records and highly imbalanced datasets. Performance evaluation was carried out using metrics such as accuracy, precision, recall, and F1-score to ensure reliable classification results. The experimental results demonstrate that machine learning models can effectively identify suspicious insurance claims and support insurance companies in minimizing fraud-related losses. Future enhancements may include ensemble learning, deep learning approaches, and real-time fraud detection systems for improved performance and scalability.
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