done through parametric models. Machine learning can push the boundaries of discrete choice modeling for policy … knowledge of how well machine learning methods can estimate individual discrete choice rules under individual heterogeneity …, especially in the context of challenges often experienced during preference elicitation. This study evaluates four machine …

techniques, combined with machine learning algorithms, to improve predictive accuracy for rare, high-impact wildfire events …

unsupervised machine learning to identify reporting anomalies for firms. Our findings suggest that higher financial anomaly risk is …

false alarms. This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud. In … addition, classical machine learning methods must be extended, minimizing expected financial losses. Finally, fraud can only be … overcome these challenges: machine learning-based fraud detection, economic optimization of machine learning results, and a …

This article is an introduction to machine learning for financial forecasting, planning and analysis (FP&A). Machine …. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular … the naive application of machine learning usually fails in this context, the recently developed double machine learning …