certifications. We employed a quantitative, cross-sectional methodology using three machine learning algorithms (Decision Tree … Classification, Random Forest, Logistic Regression) and the double machine learning technique to identify causal effects and …

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 …

machine learning interpretability tools, we show that the variables closely tied to bank profitability and solvency are … of distress derived from banks' headroom above regulatory requirements, we investigate the performance of three machine … learning techniques against the traditional logistic model. We find that the random forest model shows superior performance …

This paper explores the application of machine learning (ML) techniques to nowcast the monthly year-over-year growth …

discharge days to form intervention and control groups. We apply two causal machine learning techniques (generalized random …

decisions, food security, and ensuring the sustainability of agriculture worldwide. While traditional machine learning … algorithms have already been used in crop yield modelling, previous research used machine learning algorithms with default … analysed in scientific literature. We present a machine learning based wheat yield estimation model using comprehensive UAV …