Predictive Modeling

THE EVOLUTION OF ACTUARIAL SERVICES

EverBright provide predictive modeling process that entails developing, testing, and validating models to forecast future events by leveraging historical data. These models utilize statistical methods and machine learning algorithms to identify patterns and relationships within datasets. In actuarial consulting, predictive modeling finds key applications in areas such as insurance pricing and underwriting, claims management, customer retention and acquisition, and enterprise risk management (ERM). For insurance pricing and underwriting, predictive models aid in setting premium rates based on risk factors and assessing the likelihood of future claims to help underwriters evaluate risks. In claims management, these models are used for fraud detection and reserve estimation. Customer retention and acquisition benefit from predictive modeling through churn analysis and targeted marketing efforts. In ERM, predictive modeling assists in risk identification and capital allocation optimization. Various techniques and tools are employed in predictive modeling, including statistical methods like regression analysis and time series analysis, as well as machine learning algorithms such as decision trees, neural networks, and support vector machines. Data preparation and processing steps involve data cleaning, feature engineering, and normalization to ensure model effectiveness. Model validation and performance assessment are crucial, with techniques like cross-validation and evaluation metrics like Mean Squared Error (MSE) and Area Under the Curve (AUC) being utilized. We use software and tools for predictive modeling in actuarial consulting include R, Python, SAS, TensorFlow, Keras, Scikit-Learn for machine learning, and Tableau, Power BI for data visualization.