Risk Estimation and Management
For a niche insurance company operating in a medical/health domain we developed a prediction machine learning-based model to estimate the risk of an economical loss. The machine learning model is based on neural networks and built by consuming historical medical data over five consec-utive years. The developed model significantly outperformed statistical approaches. With this mod-el, the customer was able to optimize its pricing policies which resulted in significant savings.
The insurance company operating in a medical/health domain was facing the challenge of pricing policies development. For them, it was important to understand risks related to a particular patient and adjust pricing policy models accordingly. In turn, the customer was expecting to experience considerable savings.
Solution by AI Superior
We built an application based on a machine learning model to predict the probabilities of a particular disease according to many input features and parameters including medical history. For that, we trained a deep learning model that was effectively dealing with intrinsic challenges such as class im-balance. Additionally, we built a validation framework to objectively compare multiple approaches and ensure that the created model was significantly outperforming others.
Outcome and Implications
The developed Data Science solution significantly outperformed the baseline models relying on statistics. The model outcome was used to optimize pricing policy to increase revenue and better manage risks.