Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by...

We propose a novel approach for loss reserving based on deep neural networks. The approach allows for joint modeling of paid losses and claims outstanding, and incorporation of heterogeneous inputs. We validate the models on loss reserving data across lines of business, and show that they...

The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development. This is best understood against the context of the evolution of these...

Recent survey evidence suggests that investors form beliefs about future stock returns by predominantly extrapolating their own experience: They overweight returns they have personally experienced while underweighting returns from earlier years and consequently expect high (low) stock market...

Early detection of heart complications is highly effective in treating patients with cardiovascular diseases. Various machine learning methods have previously been used for the early detection of heart diseases. However, existing data-driven machine learning (ML) approaches fall short of...

Innovations in statistical technology have sparked concerns about distributional impacts across categories such as race and gender. Theoretically, as statistical technology improves, distributional consequences depend on how changes in functional forms interact with cross-category distributions...