We accomplish this by continually adding new and robust data sources in quantities that are needed for state-of-the-art neural-network and gradient boosting ensembles. That’s a fancy way of saying that our models can combine several different predictive models to be used in combination with each other to provide more accurate predictive outputs.

Gradient Boosting is an ensemble method that combines multiple “weak” models, such as decision trees, to create a more powerful “strong” model. It works by repeatedly training new models to correct the mistakes of previous models, with the goal of reducing the overall error rate.

Neural network ensembles are a group of neural networks that work together to make a prediction. This can be done by combining the outputs of multiple networks, or by training different networks on the same dataset and using their combined predictions. The idea behind using ensembles is that the combined predictions of multiple models can be more accurate than the predictions of any individual model.

By employing these modeling techniques, you can be assured that by using Verikai you are getting top of the line modeling outputs to make better underwriting decisions for your business.

Predictive modeling can improve insights into underwriting risk for insurance companies by providing a more accurate assessment of the risk associated with a particular policyholder or group of policyholders. By using historical data on claims, policyholder demographics, behavioral data, and other relevant factors, predictive models can identify patterns and trends that may indicate a higher or lower risk of claims.