# A Bagged Ensemble Convolutional Neural Networks Approach to Recognize Insurance Claim Frauds
## 5 Conclusions  
In this work, a bagged ensemble learning-based model is proposed to recognize frauds in auto insurance claims. Three pre-trained CNN models are selected at first, which are minimized by reducing the redundant blocks of layers, and then tweaked to work on 1D data. Along with these pre-trained models, a lightweight CNN model is proposed, consisting of only 19 layers to efficiently work on 1D data. The selected dataset is randomly split into training, validation, and testing portions, so that each CNN model is trained on different data. At the end all CNN models are stacked in parallel, so that the properties of each model can be utilized at the same time. Once a decision is made by each model through the SVM classifier, a majority pooling approach is used to obtain a combined output. The proposed method is tested on publicly available real-word dataset and achieved promising results. The experiments show that used approach boosted the accuracy of the proposed individual 1D CNN, achieving an accuracy of 98% and a low Brier Score Loss of 2%. Due to the imbalanced nature of the dataset, other metrics were also used, namely, balanced accuracy and F1 score. the proposed approach achieves a score of 98% on both metrics, showing the model's ability to both detect fraudulent and legitimate claims and be accurate with the captured cases.  
Figure 16: Confusion matrix of the proposed Bagged ensemble learning based model.  
Figure 17: Recal, precision and F1 score of our final bagged ensemble learning based model.  
The outcomes of our investigation show how successful the suggested approach is at spotting false insurance claims. Our approach dealt with an unbalanced dataset and still managed to attain a high level of accuracy. Additionally, the model can learn sophisticated data representations and strengthen its robustness thanks to the usage of CNNs and a bagged ensemble learning method. This study demonstrates the value of applying ensemble learning techniques to the detection of fraudulent insurance claims and opens up possibilities for future study.  
Finally, for our future research work, we will investigate how to employ the federated learning approach in order to increase efficiency. In addition, a novel data augmentation approach can also be employed to decrease the impact of imbalanced data. Other pre-trained CNN models can also be employed to increase the performance of the proposed model.  
Conceptualization, Y.A.; Methodology, Y.A.; Validation, M.L.; Writing--original draft, Y.A.; Writing--review and editing, M.L.; Supervision, M.L. and A.A.; Project administration, A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset is accessible under CC0: Public Domain license, and can be downloaded on Kaggle at: [[https://www.kaggle.com/datasets/shivamb/vehicle-claim-fraud-detection](https://www.kaggle.com/datasets/shivamb/vehicle-claim-fraud-detection)]([https://www.kaggle.com/datasets/shivamb/vehicle-claim-fraud-detection](https://www.kaggle.com/datasets/shivamb/vehicle-claim-fraud-detection)) (accessed on 1 November 2022).  
The authors declare no conflict of interest.