# A Bagged Ensemble Convolutional Neural Networks Approach to Recognize Insurance Claim Frauds
## 3 Materials and Methods
### Dataset  
According to multiple surveys published in the Coalition Against Insurance Fraud website [45], nearly one in four Americans believe that defrauding insurance is acceptable. If they knew they could get away with it, about one out of every ten Americans would commit insurance fraud. Nearly one in four Americans believe that defrauding insurance is acceptable. One in ten persons feel that it is acceptable to file claims for goods that are not lost, damaged, or destroyed, or for personal injuries that never happened. Two out of every five individuals say they are \"not very likely\" or \"not likely at all\" to report someone for defrauding an insurance company. A total of 29 percent of Americans say they would not report an insurance scam committed by a friend. The real dataset used in this reseach study was released by an American insurance company, has 15,420 items with various parameters, such as accident information, automobile model, insurance type, and fault, among others. This dataset's target class, FraudFound, determines whether a submitted application is fraudulent. There are a total of 32 columns in this dataset, which provide different types of information regarding the claim. Out of these columns, the policy number is just a unique number for each entry, which represents nothing. Thus, this column is not considered important to decide the fraudulent case. The dataset is further analyzed to find unique values against each column. One row has been purposefully removed because it was seen that columns such as MonthClaimed and DayOfWeekClaimed, contain zeros. The Age column is evaluated in the following phase, where a total of 319 rows had a zero value. Instead of utilizing a mean value, the aim is to impute the data logically, by examining the AgeOfPolicyHolder field. AgeOfPolicyHolder zero values are all between 16 and 17 years old. As a result, a further investigation is performed to see if the subjects with age zero were of legal driving age or not. It was identified that the mean age is lower than the range of each AgeOfPolicyHolder until the 36 to 40 range. This finding revealed that while there are a majority of guilty cases, not all of them are; therefore, we cannot draw the conclusion that zeros are minors (under driving age). As a result, it is assumed that 16 years old is the minimum legal driving age. A graphic depiction of the association between age and fraud is shown in Figures 2 and 3. The previous imputation technique produces a lot of noise, as the preceding image illustrates. Therefore, analysis is conducted by examining the dataset's representation of the 319 indicated values. Following the investigation, it was also shown that just 3.36% of fraud cases started at age 0 overall. Given that they are a minority, it was chosen to delete those values from the dataset so that they wouldn't affect the suggested algorithm's effectiveness. By eliminating the columns with age values 0, the rows with values 0 in the MonthClaimed and DayOfWeekClaimed columns are also removed. It is then noticed that PolicyType is a string created by concatenating BasePolicy with VehicleCategory. The type of insurance is a perfect fit; however, the car type is not. So that a proper comparison between VehicleCategory and BasePolicy can be conducted, two new columns are created from PolicyType. Following this, the initial BasePolicy column and the newly generated BasePolicy2 column are compared, and there are no mismatches discovered. This results in the BasePolicy2 column being identical to the BasePolicy column. However, a total of 4849 discrepancies are discovered when the VehicleCategory is compared to the VehicleCategory2. As a result, it was decided to remove the original PolicyType column and keep the newly created columns, BasePolicy and VehicleCategory. It is obvious that there is a difference in the numbers of sedans and sports cars, but it is difficult to draw conclusions because it is unknown what type of vehicle falls into each category.