AI-Powered Financial Fraud Detection

It is a cutting-edge classification model built using advanced machine learning techniques, such as gradient-boosted decision trees (e.g., XGBoost or LightGBM) or deep learning architectures like feedforward neural networks. The model is trained on labeled transactional data, leveraging features such as transaction amount, frequency, location, device metadata, and user behavior.

The model incorporates anomaly detection techniques, ensemble learning for improved accuracy, and temporal features to capture evolving fraud patterns. Additionally, explainable AI methods like SHAP (SHapley Additive exPlanations) are integrated to ensure transparency in decision-making, enabling stakeholders to understand why specific transactions are flagged as fraudulent. This allows businesses to take proactive, data-driven measures against financial fraud.