The objective of this project is to design an AI-based secure payment system that detects fraudulent transactions in real time. The system uses machine learning techniques to analyze transaction patterns and assign dynamic risk scores for preventing unauthorized financial activities.
Study fundamentals of digital payment fraud and fraud analytics.
Collect sample datasets of legitimate and fraudulent transactions.
Identify important features such as transaction amount, time, device ID, IP address, and location.
Perform data preprocessing including normalization and handling missing values.
Implement machine learning algorithms such as Logistic Regression or Random Forest.
Train and test the fraud detection model using proper validation techniques.
Develop a risk scoring mechanism based on model predictions.
Integrate the model with a simulated payment gateway system.
Generate real-time alerts for high-risk transactions.
Evaluate system performance using accuracy, precision, recall, and F1-score.
Document improvements and scalability considerations.
