Credit Card Fraud Detection ||
Автор: Night Coder
Загружено: 2025-05-25
Просмотров: 9
Описание:
Credit Card Fraud Detection
Objective:
To build a real-time intelligent system using machine learning for detecting fraudulent credit card transactions. It aims to reduce financial loss and improve transaction security.
Approach:
Train machine learning models on historical transaction data.
Analyze patterns of fraudulent behavior (e.g., unusual time, foreign location).
Preprocess data (normalize, balance classes).
Evaluate with precision, recall, F1-score.
Deploy the best-performing model via REST API.
Tech Stack & Architecture:
Machine learning models (Random Forest, XGBoost)
Data balancing techniques like SMOTE
REST API for real-time integration
Visualization dashboard for monitoring
Feasibility:
Technical: Proven ML methods make this achievable.
Economic: Uses open-source tools for cost efficiency.
Operational: Easy API integration into existing systems.
Viability: Aligns with industry trends in fraud prevention.
Benefits:
Enhances transaction security.
Reduces financial fraud losses.
Builds customer trust in digital payments.
Scalable and efficient with automation.
Helps meet regulatory compliance.
Continuously improves with feedback and retraining.
Conclusion:
The project demonstrates the effectiveness of ML in combating credit card fraud. It’s a scalable, adaptive, and secure solution ready for real-world deployment, helping financial institutions provide safer digital payment experiences.
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