Optimized Insurance Sales Predictions with SaleNet-CG Powered by CNN-GRU Hybrid Framework

Precise prediction of agent sales performance is a critical task for strategic planning in the dynamic insurance business. Conventional machine learning algorithms frequently fail to adequately capture short-term behavioral signals as well as long-term temporal dynamics present in agent activity data. To mitigate this, the research introduces a hybrid deep learning framework referred to as SalesNet-CG, where the model integrates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). CNN layers detect local behavior patterns, and GRU layers capture sequential dependencies over time. Preprocessing of the dataset was conducted using missing value imputation, outlier treatment, temporal feature extraction, and normalization to maintain data quality. Experimentation proved that SalesNet-CG attained a prediction accuracy of 98.73% surpassing baseline models. Such high accuracy indicates the model's capacity for identifying subtle pattern behavior associated with sales success. The findings affirm the promise of the CNN-GRU model as a valuable tool in insurance performance forecasting, providing decision-makers with actionable insights.