Auto-Insurance Fraud Detection Using Machine Learning Classification Models

Toluwalope Toluwalope, Essa Shahra, Shadi Bassura

    Research output: Contribution to journalArticlepeer-review


    The major goal of water management planning and the iterative evaluation of operational policies and procedures is to ensure that good water quality is always maintained. Effective water monitoring requires examining many water samples, which is a time-consuming and labour-intensive process that takes a lot of effort. This paper aims to evaluate the quality of drinking water samples with high accuracy by using multi-class classification models: multilayer perceptron (MLP) and ensemble learning. Real datasets with different sizes that include the essential water quality parameters have been used to train and test the developed models. The results showed the effectiveness of the developed models in detecting water contamination with high accuracy in both datasets used. The results demonstrate that bagging Ensemble learning outperforms the multilayer perceptron with an overall accuracy of 94% for station-A and 92% for station-B compared to MLP, which shows an overall accuracy of 89% for station-A and 87% for station-B.
    Original languageEnglish
    Pages (from-to)1-10
    Number of pages10
    Journal8th International Congress on Information and Communication Technology
    Publication statusPublished (VoR) - 26 Oct 2023


    • Fraud Detection
    • Classification
    • Machine Learning
    • Random Forest


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