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...

Bankruptcy prediction is always a topical issue. The activities of all business entities are directly or indirectly affected by various external and internal factors that may influence a company in insolvency and lead to bankruptcy. It is important to find a suitable tool to assess the future...

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....

Novel approaches employing an Artificial Neural Networks to enhance the infrastructure of existing Monte Carlo Risk engines are presented. An Artificial Neural Network is utilized to retrieve trade- and market data from existing Expected Exposure profiles of interest rate swaps which enables its...

This research article presents a comparative analysis between logistic regression as a traditional method, artificial neural networks (ANNs), and decision tree as machine learning techniques for predicting credit risk. It meticulously examines and evaluates these three methods, elucidating their...

The study's objective is to check whether the predictive power of Machine Learning Techniques is better than Logistic Regression in predicting the bankruptcy of firms and that the same predictive power of ascertaining bankruptcy improves when a proxy for uncertainty is added to the model as a...