Abstract: A method and system are provided for generating a combined prediction using an ensemble machine learning system. The prediction may be used in risk assessment for payroll processing. A data point is received as input to a multitude of trained models. Each model is trained from a respective data subset of a disparate data. A model prediction this generated by each of a multitude of machine learning models. For each respective trained model, a trust score is generated based on a data sparseness metric of the data point and a feature importance vector of the respective model. The model predictions and trust scores are received as input to a meta-model that was trained from the trust score and the model prediction of the multitude of trained models over the respective data subset of the disparate data. A combined prediction is generated using the trained meta-model.