# A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket Activities
## 5 Profile-Based Fraud Detection
### Credit Card Profiling  
This profiling mechanism focuses on the past activities of a given credit card. It scans the entire history of the respective card, as given in Table 2. Thus, we collect feedback about the card used for purchasing an airline ticket and we form an opinion about the reliability of this card. The following fields are extracted as novice features from the past transactions of the given card:  
* Number of chargebacks in the past
* Number of accepted transactions in the past
* Number of rejected transactions in the past
* Number of transactions submitted for manual review in the past
* Number of domestic flights purchased with this credit card
* Number of international flights purchased with this credit card
* Number of unique phone numbers used with this credit card
* Number of unique emails used with this credit card \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Transaction**} & \multirow{2}{*}{**Fraud**} & \multirow{2}{*}{**Approval**} & \multirow{2}{*}{**Chargeback**} & \multirow{2}{*}{**Transaction**} & \multirow{2}{*}{**Flight Date**} & \multirow{2}{*}{**Credit Card**} & \multirow{2}{*}{**Passengers**} & \multirow{2}{*}{**Hunings to**} & \multirow{2}{*}{**Passenger**} \\  & & & & & & & & & **Flight** & **Matched** \\ \hline T1 & No & Accept & No & 21/12/2022 & 28/12/2022- & [PERSON] & [PERSON] & 10,530 & Yes \\  & & & \(-\)11:30 & 19:00 & [PERSON] & & & & \\ \hline T2 & No & Accept & No & 15/02/2023— & 28/06/2023— & Thomas & Madelyn & 191,718 & Yes \\  & & & 10:42 & 19:00 & Johnson & Johnson & 191,718 & Yes \\ \hline T3 & No & Accept & No & 06/08/2022— & 02/09/2022— & [PERSON], & [PERSON], & 37,896 & Yes \\  & & & & 22:24 & 06:00 & [PERSON] & & & \\ \hline \multirow{2}{*}{**T4**} & \multirow{2}{*}{**Tran**} & \multirow{2}{*}{**Tran**} & \multirow{2}{*}{**Tran**} & \multirow{2}{*}{**Tran**} & \multirow{2}{*}{**Tran**} & \multirow{2}{*}{**Tran**} & \multirow{2}{*}{**Tran**} & \multirow{2}{*}{**Tran**} & \multirow{2}{*}{**Tran**} \\  & & & & & & & & & \\ \hline T5 & No & Accept & No & 10/02/2023— & 02/03/203/203— & Leonie & Rosanna & 28,373 & Yes \\  & & & 14:22 & 07:15 & Larson & Larson & & \\ \hline T6 & No & Accept & No & 28/09/2022— & 14/11/2022— & Mariane & Manielle & Danielle 67,774 & Yes \\  & & & 08:56 & 10:30 & Wallace & Wallace & 67,774 & Yes \\ \hline T7 & No & Accept & No & 07/05/2023— & 25/05/2023— & Findlay & Findlay & 25,728 & Yes \\  & & & 11:38 & 14:55 & [PERSON] & & [PERSON], & & \\ \hline T8 & No & Accept & No & 25/12/2022— & 01/01/2023— & [PERSON] & [PERSON], & 9006 & Yes \\  & & & 22:24 & 04:30 & [PERSON] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Nine independent record profiling scenarios to emphasize the difference in the capability of fraud detection between transactional and profile-based approaches. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**PNR Profile**} \\ \hline \multirow{2}{*}{**Transaction**} & \multirow{2}{*}{**Fraud**} & \multirow{2}{*}{**Approval**} & \multirow{2}{*}{**Chargeback**} & \multirow{2}{*}{**Email**} & \multirow{2}{*}{**Phone**} & \multirow{2}{*}{**Accept**} & \multirow{2}{*}{**Reject**} & \multirow{2}{*}{**Chargeback**} & \multirow{2}{*}{**Unique**} & \multirow{2}{*}{**Used Phone**} \\  & & & & & & & & & & **Email** & **Numbers** \\ \hline T1 & Yes & Record & No & reduced30-400-4000 & 1000 & 0 & 0 & 0 & 0 \\ \hline T1 & Yes & Record & No & reduced30-400-4000 & 1000 & 0 & 0 & 0 & 0 & 0 \\ \hline T2 & Yes & Record & No & reduced30-4000-4000 & 1000 & 0 & 0 & 0 & 0 & 0 \\ \hline T3 & Yes & Record & No & reduced30-4000-4  
* Average amount spent with this credit card
* Highest amount spent with this credit card  
Table 2 contains two credit card profiling scenarios that present the status of the profiling map when each transaction arrives. These scenarios represent how cases that cannot be detected from a transactional perspective can be caught using changes in spending habits from a profile-based perspective.