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

Transaction fraud imposes serious threats to e-commerce shopping. As the online transaction is becoming more well known the types of online transaction frauds associated with this are likewise rising which affects the money related industry. This fraud detection system has the ability to restrict and hinder the transaction performed by the attacker from a 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 the required data which is efficient to detect fraudulent user action. The details of items purchased by any Individual transaction are generally 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. A FDS runs at a credit card giving bank. Each approaching 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 products that are purchased in that transaction are not known to the FDS. The bank declines the transaction if FDS affirms the transaction to be a fraud. User spending patterns and geographical area is used to verify the identity. In the event that any surprising pattern is detected, the system requires re-verification. Based on previous information of that user, the system recognizes uncommon patterns in the payment procedure. After 3 invalid attempts, the system will hinder the user.