Browsing by Author "Muthura, Brian Ndirangu"

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    A Hybrid Model for Detecting Insurance Fraud Using K Means and Support Vector Machine Algorithms
    (Kenyatta University, 2024-10) Muthura, Brian Ndirangu
    Medical insurance fraud is a significant issue in the healthcare sector, commonly characterized by fraud patterns such as misrepresentation of services, false claims, and identity theft. These patterns contribute to severe data class imbalances, with legitimate claims vastly outnumbering fraudulent ones, complicating effective detection. Current fraud detection methods struggle to address these evolving patterns and manage imbalanced datasets. This study employs a mixed-methods approach, integrating an extensive literature review with quantitative analysis of historical medical claims data. The research develops and evaluates four machine learning models: a standalone Support Vector Machine (SVM), a tuned SVM, a hybrid model combining K-Means clustering with SVM, and a tuned hybrid model. The models were compared using key metrics, including accuracy, precision, recall, and F1 score. Results show that the tuned hybrid model achieved the highest performance with an accuracy of 97.49%, demonstrating its superior ability to detect fraudulent claims compared to the standalone and default hybrid models. Future work will focus on further improving the computational efficiency of the hybrid model and exploring its adaptability to new and evolving fraud patterns in real-time environments. This research significantly advances fraud detection by offering a robust solution that tackles class imbalances and adapts to evolving fraud schemes
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    A Hybrid Model for Detecting Insurance Fraud Using K-Means and Support Vector Machine Algorithms
    (Open Journal for Information Technology, 2023) Muthura, Brian Ndirangu; Matheka, Abraham
    Private stakeholders and governments across the globe are striving to improve the quality and access of healthcare services to citizens. The need to improve healthcare services, coupled with the increase in social awareness and improvement of people’s living standards, has seen an increase in medical policyholders in the insurance industry. Even so, the healthcare sector is grappled with increased costs every other year, leading to revision of premiums and increased costs for the policyholders. One of the main factors contributing to the increased costs is fraudulent claims raised by the service providers and the policyholders, leading to unprecedented risks and losses for insurance firms. The insurance industry has set up fraud detection and mitigation systems to mitigate losses brought about by fraudulent claims, which come in two flavors: rule-based systems and expert claims analysis. With rule-based systems, conditions such as missing details, location of the claim vis a vis the location of the policyholder, among other rules, are evaluated by systems to assess the validity of the claims. On the other hand, insurance firms rely on the human intervention of experts using statistical analyses and artificial rules to detect fraudulent claims. The rule-based and expert analysis methods fail to detect patterns or anomalies in claims, which is central to efficient fraud detection. Data mining and machine learning techniques are being leveraged to detect fraud. This automation presents enormous opportunities for identifying hidden patterns for further analysis by insurance firms. This research aims to analyze a hybrid approach to detect medical insurance fraud using both K Means (unsupervised) and Support Vector Machines (supervised) machine learning algorithms.