Online Transaction Fraud Detection using Python & Backlogging on E-Commerce

Transaction fraud imposes serious threats on e-commerce shopping. As online transaction is becoming more popular the types of online transaction frauds associated with this are also rising which affects the financial industry. This fraud detection system has the capability to restrict and block the transaction performed by the attacker from genuine user’s credit card details. To overcome these problems, this system here is developed for the transactions higher than the customer’s current transaction limit. During registration we take required information which is efficient to detect fraudulent user activity. The details of items purchased by any Individual transaction are usually not known to any Fraud Detection System (FDS) running at the bank that issues credit cards to the cardholders. BLA (Behavior and Location Analysis) is implemented for addressing this problem. An FDS runs at a credit card issuing bank. Each incoming transaction is submitted to the FDS for verification. FDS receives the card details and transaction value to verify, whether the transaction is genuine or not. The types of goods that are bought in that transaction are not known to the FDS. Bank declines the transaction if FDS confirms the transaction to be fraud. User spending patterns and geographical location is used to verify the identity. If any unusual pattern is detected, the system requires re-verification. Based upon previous data of that user, the system recognizes unusual patterns in the payment procedure. After 3 invalid attempts the system will block the user.