In this session, we present a cutting-edge privacy-preserving machine learning technique that fosters collaborations between insurance companies and InsurTech firms. Our presentation consists of two parts. The first part delves into the inner workings of deep learning and establishes it as a powerful tool that could replicate predictions from a diverse array of machine learning techniques. The second part introduces the federated learning based on the architecture of neural networks. Using a case study in insurance industry, we demonstrate how privacy-preserving collaboration can enhance claims prediction.
Learning Objectives:
Comprehend the power of deep learning.
Navigate federated learning techniques.
Apply privacy-preserving collaboration in insurance.
