Python
Aim:
To develop a robust and scalable fraud detection framework for Unified Payments Interface (UPI) transactions using advanced ensemble and boosting algorithms such as Random Forest, Extra Trees, Cat Boost, and Light GBM.
Abstract:
With the exponential rise in Unified Payments Interface (UPI) transactions, fraud detection has become a critical concern for financial institutions. Existing detection systems are limited by high false positives and poor adaptability to evolving fraud tactics. This study proposes an advanced fraud detection system incorporating Random Forest, Extra Trees, CatBoost, and LightGBM. These models leverage ensemble and gradient boosting mechanisms to identify suspicious transaction patterns with greater precision. The proposed framework integrates preprocessing, anomaly detection, and optimized machine learning algorithms for real-time fraud prediction. Experimental validation shows improved accuracy, scalability, and reduced false positives, offering a secure and efficient solution for digital payments.
Proposed System:
The proposed system applies Random Forest, Extra Trees, CatBoost, and LightGBM to UPI transaction data. These algorithms utilize ensemble learning and gradient boosting to capture complex patterns and enhance classification accuracy. The system architecture includes data preprocessing, feature engineering, real-time fraud detection, and a web-based interface for monitoring. This framework ensures adaptability, accuracy, and scalability for securing UPI transactions.
Advantage:
1.Improved fraud detection accuracy with ensemble and boosting models.
2.Reduction of false positives using robust anomaly detection.
3. Real-time detection capability for immediate alerts.
4. Scalable system architecture suitable for large datasets.
5. Continuous adaptability to new fraud strategies.
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