This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...

In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these...

Predicting if a client is worth giving a loan-credit scoring-is one of the most essential and popular problems in banking. Predictive models for this goal are built on the assumption that there is a dependency between the client's profile before the loan approval and their future behavior....