Credit Card Fraud Detection ||

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.