Credit Card Fraud Detection

We propose to design a system which does not require fraud signatures and yet is able to detect frauds by considering a cardholder?s spending habit. Card transaction processing sequence by the stochastic process. The details of items purchased in Individual transactions are usually not known to any

Project Title

Credit Card Fraud Detection

Project Area of Specialization

Artificial Intelligence

Project Summary

We propose to design a system which does not require fraud signatures and yet is able to detect frauds by considering a cardholder’s spending habit. Card transaction processing sequence by the stochastic process. The details of items purchased in Individual transactions are usually not known to any Fraud Detection System (FDS) running at the bank that issues credit cards to the cardholders. Another important advantage is a drastic reduction in the number of False Positives transactions identified as malicious by an FDS although they are actually genuine. Each incoming transaction is submitted to the FDS for verification. FDS receives the card details and the value of purchase 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. It tries to find any anomaly in the transaction based on the spending profile of the cardholder, shipping address, and billing address, etc. If the FDS confirms the transaction to be of fraud, it raises an alarm, and the issuing bank declines the transaction.

The credit card fraud detection features uses user behavior and location scanning to check for unusual patterns. These patterns include user characteristics such as user spending patterns as well as usual user geographic locations to verify his identity. If any unusual pattern is detected, the system requires re-verification.

The system analyses user credit card data for various characteristics. These characteristics include user country, usual spending procedures. Based upon previous data of that user the system recognizes unusual patterns in the payment procedure. So now the system may require the user to login again or even block the user for more than 3 invalid attempts.

Project Objectives

The key objective of credit card fraud detection system is to identify suspicious events and report them to an analyst while letting normal transactions be automatically processed

Project Implementation Method

1. Gathering Data
2. Test Data
3. Training Data
4. Model Training
5. Test Model Predictions
6. Model Deploy
7. Evaluation

Benefits of the Project

• Due to Behavior and location analysis approach, there is a drastic reduction in the number of False Positives transactions identified as malicious by an FDS although they are actually genuine. The system stores previous transaction patterns for each user. Based upon previous data of that user the system recognizes unusual patterns in the payment procedure. The System will block the user for more than 3 invalid attempts.
• Higher accuracy of fraud detection
• Less manual work needed for additional verification
• Fewer false declines
• Ability to identify new patterns and adapt to changes

Technical Details of Final Deliverable

In this work we aim and target towards false transaction that occur on daily basis. We intend to stop these vulnerabilities so that online systems be more secure. The proposed system will be comprising of a credit card machine that will be connected to internet and will be integrated to a banking system. This banking will also a part of our project because the banks will not provide their data due to security reasons.

All the transaction will be generating a data set that will be used to feed our proposed Machine Learning based algorithm and it will analyse that either the requested information invalid or invalid. If in case the transaction is found to be invalid the amount that has be requested from a particular bank account will not be deducted.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Security

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

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