# A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket Activities
###### Abstract  
Although the most widely studied datasets in fraud-detection systems belong to the banking sector, the aviation industry is susceptible to fraud activities that seriously harm airline companies. Therefore, big airline companies have started to purchase or develop their own fraud-detection systems in order to prevent their financial loss and prestige decline. Chronological order and temporal flow are intrinsically of high importance in fraud detection in the banking sector as well as in airline sale channels. Therefore, the transactions in the datasets used in fraud-detection systems should be evaluated not only according to the information they contain but also according to the past transactions they are linked to. One of the best ways to raise awareness about the connected past transactions to the fraud-detection system is to profile the data fields whose historical data is important and dynamically place these profiles on each transaction. In this study, we first draw the baseline, i.e., the first touch in this field, for fraud detection in aviation and then introduce a novel multi-modal profiling mechanism based on deep learning for the detection of fraudulent airline ticket activities. We achieved great success by feeding the new features obtained from those profiles into a deep neural network that is fine-tuned by adjusting the well-known hyperparameters regarding the aviation data. Thanks to the combination of profiling and deep learning, the F1 score of the proposed system reaches up to 89.3% and 93.2% in terms of quantity-based success and cost-based success, respectively. fraud detection; aviation industry; profile-based systems; machine learning; deep learning; imbalanced dataset; cost-sensitive measurement; history-aware detection +
Footnote †: journal: _aciences_  
have become prominent [3; 4]. However, these learning methods alone do not provide the desired effectiveness for an ideal fraud-detection system. Fraud detection is a complex issue that needs to be focused on many different points [5] and problems, especially regarding the sector facts and dynamics. One of the main problems for fraud-detection research in the literature is that the well-known datasets consist of individual transactions. However, since fraud is a temporal and chronological problem, the historical links of the transaction are also of great importance. Information about a single transaction may not always be sufficient to detect fraud. Hence, when evaluating a transaction, possessing information about its associated previous transactions enhances the historical robustness of the fraud-detection system. While there can be multiple avenues to leverage this strength, one of the most effective approaches involves transforming traditional systems into profile-based solutions. This transformation is achieved by crafting substantial profiles that can directly influence the detection mechanism through the utilization of historical data associated with each profile. In this study, we introduce a novel profile-based fraud-detection system for suspicious airline ticket activities in order to differentiate a fraudster from an ordinary airline passenger using his/her past activities. In this system, we put forward various historical and in-record profiles by introducing discriminative time-line features that have a direct impact on the success of fraud detection in the airline industry. These additional fields make it possible for each transaction to be aware of the related transactions before itself. In addition, the combinations of these designed profiles were analyzed in order to reveal the superior profile combination for the aviation field. From the perspective of companies exposed to fraud cases, fraud is a financial problem. Therefore, the primary purpose of developing fraud-detection systems is to reduce financial losses and prevent potential decreases in revenue. However, in most studies [6; 7], the success results are calculated based on the quantity for both traditional and automated detection systems.