Credit Card Fraud Detection and Prevention By Face Recognition
Abstract : Internet banking is now becoming the most commonly used form of banking transactions. Confidentiality can be compromised in the process of electronic purchases.
We therefore introduced a new approach to prevent theft during online transactions in order to protect information through a two-step mechanism of authentication.
The primary step of authentication is OTP verification. If the OTP has been checked, the face should be recognized. The details are collected and the authorization for both true and fraudulent transactions is submitted to the bank.
The new credit card scanning device has beneficial characteristics such as certain health, user-friendliness, etc. The purpose of the application is to reduce credit card fraud by knowledge of the Face System. Customers get the most accessible and highly efficient electronic banking program.
These systems use predefined rules and thresholds to flag suspicious transactions. Rules can include parameters like transaction amount, frequency, and location.
These systems use algorithms and models that learn from historical data to identify patterns and anomalies indicative of fraud.
This approach analyzes patterns of user behavior, such as spending habits and transaction frequency, to detect deviations that may indicate fraud.
Continuous monitoring of transactions in real-time or near-real-time to identify suspicious activities based on various criteria.
Limited Flexibility: Rules need constant updating to adapt to new fraud patterns. Outdated rules can lead to missed fraud or false positives.
High False Positives: Legitimate transactions may be flagged as fraudulent, causing inconvenience for users.
Scalability Issues: As transaction volumes grow, managing and updating rules can become cumbersome.
Data Quality Dependence: The effectiveness of machine learning models depends heavily on the quality and quantity of training data. Poor data can lead to inaccurate predictions.
Complexity and Maintenance: Machine learning models require ongoing training, fine-tuning, and monitoring to remain effective, which can be resource-intensive
This paper proposes to inspect the use of facial recognition for login and for banking purposes. The potency of our system is that it provides strong security, username and password verification, face recognition and pin for a successful transaction.
Multilevel Security of this system will reduce problems of cyber-crime and maintain the safety of the internet banking system. The end result is a strengthened authentication system that will escalate the confidence of customers in the banking sector.
Biometric Authentication: Face recognition provides a biometric layer of authentication, which is more difficult to forge or steal compared to traditional methods like passwords or PINs.Unique
Identifier: Each person’s facial features are unique, providing a strong, personalized security measure that’s hard to replicate.
Seamless Authentication: Face recognition allows for quick and frictionless authentication, eliminating the need for passwords or PINs. Users can simply look at the camera to verify their identity.
No Physical Tokens Required: Unlike physical cards or devices, face recognition doesn’t require users to carry or remember anything, simplifying the authentication process.
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