# A Bagged Ensemble Convolutional Neural Networks Approach to Recognize Insurance Claim Frauds
## 3 Materials and Methods
### Comparison Paradigms  
Based on the literature, we have selected popular machine learning and statistical classifiers which are widely used in fraud detection systems. To test the effectiveness of our proposed model, those binary classification methods are used for a performance comparison. XGBoost Classifier, Decision Tree, KNeighbors, SVC, Gaussian Process, Random Forest, MLP, AdaBoost, Quadratic Discriminant Analysis, and GaussianNB are all among the ten classifiers employed in this study. A brief description of each classifier is in the following:  
**XGBoost Classifier(XGB)**: XGBoost is a gradient boosting system developed by [PERSON]. The system was originally created to improve speed and model performance. XGBoost has become the most popular machine learning system for structured or tabular datasets. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost, under the Gradient Boosting framework, provides a parallel tree boosting algorithm (also known as GBDT or GBM) that is fast and accurate. The same code can be used on different distributed environments (such as Hadoop, SGE, MPI) to solve problems with billions of examples. Due to the size of data in insurance claim fraud problems and the imbalanced nature of data, XGBoost showed promising results in this area [36]. XGBoost based model can outperformed other methods such as Support Vector Machine, Random Forest and Logistic Regression in highly imbalanced datasets [37]. **Decision Tree (DT)**: A decision tree is a type of flowchart that uses a tree-like structure to show the possible outcomes of an event or problem. The different branches of the tree represent different options or choices, and each leaf node represents a final outcome (such as success or failure). The root node is the starting point for the decision tree; it learns which attribute value to partition on based on previous data. Partitioning the tree in this way is known as recursive partitioning. In problems with highly imbalanced data, DT can perform slightly better than other algorithms. In [38], the authors used a publicly available automobile insurance fraud detection dataset and demonstrated that DT slightly out performances Support Vector Machine (SVM) and Artificial Neural Network (ANN). **KNeighbors**: The k-nearest neighbors algorithm (k-NN) is a non-parametric method that can be used for both classification and regression. For each new data point, the k closest training examples are found in the feature space. The output then depends on whether k-NN is being used for classification or regression. In the literature KNN seems to perform better than other algorithm in anomaly and fraud detection tasks. One example is in [39] where the authors examined the performance of 3 machine learning models implemented on credit card transactions to identify fraudulent behavior. The authors performed a comparative analysis using K-NN, Naive Bayes, and Logistic Regression trained on a credit card dataset designed by European cardholders containing 284,807 transactions. The finding of the comparative analysis shows that K-Nearest Neighbor outperformed LR and NB techniques. **SVM**: Support Vector Machine is one of the most widely used algorithm in financial fraud detection [40]. It is a supervised learning algorithm that can be used for both classification and regression tasks. The main idea of SVM is to create a line or decision boundary that can best segregate n-dimensional space into classes, so that new data points can be easily placed in that space. This line is called a decision boundary. **Gaussian Process (GP)**: A Gaussian process is a generalization of the multivariate normal distribution which applies to any collection of random variables, provided that only a finite number of them are considered at any one time. GP is a powerful model that can be used for anomaly detection in machine learning problems.