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Fraud Detection in Insurance Using Machine Learning
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AI
DOI：10.1007/978-981-96-7514-2_7.png)
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Abstract
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Fraudulent claims in the insurance industry lead, to significant financial losses and negatively affect both policyholders and insurance firms. Machine learning has proven to be revolutionizing fraud detection since it is more than just performing the ordinary rule-based systems while automating and optimizing detection processes. The current work proposes a novel hybrid approach that combines supervised and unsupervised techniques in machine learning with applications in accurately and robustly detecting insurance fraud. Three primary models include in the framework are Decision Tree, Random Forest, and Voting Classifier, which improve detection performance on real-world datasets. In addition, an embedding-based model interprets sequential claims data, and a statistically-validated network is used to detect patterns of collusion and fraud among related entities. Extensive experimentation was conducted using large-scale motor, and general insurance datasets and showing that the proposed hybrid model achieved an accuracy of 89.60%. Hyper-parameter tuning and data preprocessing were used to further refine the model's performance; it was able to counterbalance all issues brought forth by imbalances, complexities, and complexities due to variations in fraud types. The methodology outperformed the existing models, better at identifying rare sophisticated cases of fraud. The practical implications of deploying machine learning models in the insurance sector are also discussed from the angle of best practices for data governance, model interpretability, and stakeholder trust. In the future, this work will be improved by incorporating real-time analytics to provide quicker detection, enhancing interpretability features, and adapting the model to emerging fraud patterns in evolving data environments.
Keywords:
Machine learning
Supervised learning
Unsupervised learning
Hybrid models
Ensemble models
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