Financial Market Forecasting and Fraud Detection Using AGLT Ensemble Model

Due to the inherent volatility and complexity of the financial market, sophisticated methodologies are required for reliable forecasting and efficient fraud detection. Predictive analytics and machine learning improve anomaly identification and financial market forecasts. To obtain a thorough grasp of market dynamics, we use the AGLT model, which combines the GARCH, LSTM, Transformer, and ARIMA models. We use a variety of classifiers for anomaly identification, with an emphasis on the superior performance of CatBoost and XGBoost, as well as Logistic Regression, AdaBoost, SVC, Random Forest, and CatBoost. Our system incorporates real-time data processing that includes Spark Streaming and Apache Kafka, enabling rapid analysis and response. The solution also ensures flexibility and scalability to adjust to shifting market conditions by supporting continuous learning via automated pipelines and cloud deployment. This integrated approach not only makes decision making easier but also improves the capacity to successfully reduce market risks and stop fraudulent activity. The findings demonstrate the effectiveness of fusing strong anomaly detection methods with sophisticated forecasting models, opening the door to more dependable and secure financial market operations