AI-Based Credit Card Fraud Detection and Protection System for All-In-One Banking Applications
Author : B Asha and S Suryaprabha
Abstract :
The rapid growth of digital banking and online payment systems has significantly increased the use of credit cards for financial transactions. However, this growth has also led to a rise in fraudulent activities, making fraud detection a major concern for financial institutions. Traditional fraud detection systems often rely on rule-based methods that may fail to identify new or complex fraud patterns. To address this challenge, an AI-Based Credit Card Fraud Detection and Protection System for All-in-One Banking Applications is proposed. The system utilizes machine learning algorithms to analyze transaction data and identify suspicious activities in real time. By examining various transaction features such as spending patterns, transaction location, frequency, and amount, the model can distinguish between legitimate and potentially fraudulent transactions. Data preprocessing techniques are applied to clean and prepare transaction data, while machine learning models are trained to recognize patterns associated with fraudulent behavior. The proposed system enhances banking security by automatically monitoring transactions and generating alerts when abnormal activity is detected. This enables banks and users to respond quickly and prevent financial losses. Additionally, the system can continuously learn from new transaction data, improving its detection accuracy over time. By integrating fraud detection mechanisms into an all-in-one banking platform, the system provides a secure and efficient environment for managing financial transactions. Overall, the proposed approach demonstrates how artificial intelligence can strengthen financial security by providing faster, more accurate fraud detection and protection for digital banking systems.
Keywords :
Credit Card Fraud Detection, Artificial Intelligence, Machine Learning, Banking Security, Financial Technology.
