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The rapid growth of digital banking has significantly increased the prevalence of fraudulent activities, threatening the security of financial institutions and their customers. Conventional fraud detection systems, which rely heavily on static rule-based mechanisms, often fail to adapt to evolving fraud tactics and generate high false-positive rates. This project, Fraud Detection in Banking Data Using Machine Learning, presents an integrated framework that combines predictive modeling and optimization strategies to enhance the accuracy and efficiency of fraud detection. Historical banking transaction datasets are collected, preprocessed, and analyzed through exploratory data analysis (EDA) to uncover patterns and anomalies indicative of fraudulent behavior. Machine learning algorithms, including Logistic Regression, Decision Trees, Random Forest, and XGBoost, are applied to predict fraudulent transactions. To address the issue of class imbalance, resampling techniques such as SMOTE are employed, while hyperparameter tuning and model evaluation using precision, recall, F1-score, and confusion matrix ensure robust performance. The proposed system integrates predictive insights with optimization strategies, thereby minimizing both false positives and false negatives, and supports real-time as well as batch processing modes. Furthermore, a scalable and user-friendly deployment architecture based on web frameworks enables seamless integration into banking infrastructure. The outcome is a proactive fraud detection system that not only forecasts fraudulent activities but also optimizes detection performance, strengthening customer trust and reducing financial losses.