To protect financial and insurance information against fraudulent activity, errors, and other odd patterns, anomaly detection is an essential component. As a result, the purpose of this research is to present advanced methods for the intelligent identification and notification of anomalies that are combined with statistical methods, machine learning, and notification systems. The goals of the project are to establish a comprehensive framework for anomaly identification, limit the number of false positives, and deliver alerts in real time. Isolation Forests, Autoencoders, and XGBoost are examples of hybrid machine learning models included in the methodologies. Other methodologies include feature engineering and data preprocessing. Experiments are carried out on both synthetic and real-world datasets in order to demonstrate excellent detection accuracy and operating efficiency while simultaneously maintaining a significant reduction in the number of false positives. This study aims to highlight the potential of the suggested framework for decision-making and risk management within the fields of finance and insurance.