# A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket Activities
###### Abstract
These measurement metrics, which are based on the number of false/true detected transactions, cannot show the performance of fraud-detection systems thoroughly. For this reason, in our study, besides the transaction number-based metrics, the cost-based metrics were utilized for the evaluation of the proposed profile-based architecture. In this way, it is also possible to observe to what extent the developed detection system can achieve loss minimization and potential profit optimization. The ability of the detection system to produce both quantity-based and cost-based results is an important criterion in terms of maintaining the balance between company profitability and customer satisfaction. The scientific goals of the fraud-detection system developed in our study can be summarized in two essential points. First of all, we aim at maximizing the F1 score, which is the most important performance metric for such imbalanced problems. Since a single and static profile structure ends up with low success in fraud detection and with many false positives, we build up historical connections using existing passenger activities obtained from the proposed profiles via feature engineering. The second goal of our study is to overcome the negative effect of the randomly selected instances, as they might not represent the non-fraud data in an efficient manner. Thus, we introduce the BRUS algorithm to include the most representative instances from the airline ticket dataset. This approach has a high negative sample representation ability as well as oversampling, has no misleading synthetic positive record, and provides ease of processing thanks to its smaller size. Our study makes the following contributions to the literature:  
* To the best of our knowledge, there is no study in the literature for the aviation industry that provides solutions to fraud-detection problems and builds detection systems using modern supervised learning techniques. In this study, for the first time in the aviation field, a baseline was created using one of the biggest airline activity datasets with a specific data structure and supervised machine learning (ML) algorithms. After we created the baseline and examined the ML results, we applied deep neural network (DNN) as our main classification method to enhance the overall performance of the system. We also customized the DNN architecture utilizing different hyperparameters based on aviation data to boost its performance. * Analyzing passenger activities encouraged us to develop a multi-modal profile-based fraud-detection system in order to get rid of the restrictive structure of transactional datasets in terms of backward connection. This aviation-specific fraud-detection system can establish historical connections by taking power from three different profiles and many statistical parameters within these profiles. Therefore, it significantly increases the success of fraud detection, which is a temporal problem. * Fraud is essentially a financial problem in terms of its consequences. However, considering this problem in the literature, the number of studies using cost-sensitive measurement metrics is quite low. Due to civil aviation laws, airline companies have to regulate customer grievances, so the dimension of financial loss comes to the fore. The success of the fraud-detection system developed in this study was measured not only on the basis of quantity, but also with the cost-sensitive metrics that we recommend, and we tried to achieve a balanced success. * The balanced random undersampling (BRUS) method has been developed to solve the imbalanced dataset problem, which is the most frequently encountered problem in the detection of fraud. In this method, there is a mechanism that evenly distributes the dataset and prevents duplication during sample selection. This method was also utilized as the first step in hybrid sampling tests in our study. Section 2 presents the literature review on fraud detection. Section 3 explains our dataset and aviation terminology in general terms.