Proposed Paper Title: “AI-Driven Fraud Detection in Financial Transactions Using Hybrid Machine Learning and Cloud-Based Data Pipelines” Abstract Direction: This paper proposes a hybrid machine learning framework combining deep learning and anomaly detection algorithms to identify fraudulent transactions in real time. It leverages cloud-based data architectures (AWS, Azure, or GCP) for scalable processing and integrates generative AI models for adaptive fraud pattern recognition. The research focuses on: Enhancing the accuracy and speed of fraud detection systems in high-volume banking environments. Building a cost-efficient, cloud-native fraud monitoring pipeline using technologies such as Python, SQL, Apache Kafka, and AWS Lambda. Introducing a self-learning feedback mechanism that adapts to new fraud trends without human intervention. Demonstrating improvements such as a 35–50% reduction in false positives and significant improvement in real-time fraud response. This framework addresses critical challenges faced by financial institutions and supports U.S. national goals in financial stability, data integrity, and cyber risk mitigation. Key Methodology Components: Data Collection & Preprocessing – Simulate or use anonymized bank transaction data (Kaggle, IEEE DataPort, or proprietary datasets). Feature Engineering – Identify behavioral and transactional anomalies. Modeling Techniques – Combine: Random Forest or XGBoost (for structured fraud classification) Autoencoder or LSTM (for time-series anomaly detection) Generative AI (for synthetic fraud pattern generation) System Architecture – Implement a real-time fraud detection pipeline on cloud (AWS or Azure). Evaluation Metrics – Precision, Recall, F1-score, and AUC-ROC to measure model performance.