AI-Powered Data-Driven Approaches to Fraud Detection in Finance

Despite detection techniques have advanced, financial fraud is still a serious problem. Conventional rule-based approaches frequently suffer from high false positive rates and missed fraudulent activity because of their rigidity and vulnerability to changing fraud tactics. To improve fraud detection in finance, the study suggests a data-driven, AI-powered method. The system continuously adapts to new fraud tendencies by dynamically analyzing transactional data and utilizing machine learning (ML) algorithms for anomaly identification and predictive analytics. When it comes to real-time monitoring, essential elements include feature engineering, data preprocessing, data collecting, and model training. Its effectiveness in reducing financial risks is demonstrated by testing it against Existing systems and finding superior performance metrics in precision (0.95), recall (0.92), F1-score (0.93), and AUC-ROC (0.97). With the ever-changing financial landscape, the scalable and real-time fraud detection capabilities of the proposed system are essential for preserving transaction security and integrity