Explainable Automated Machine Learning for Credit Decisions: Enhancing Human Artificial Intelligence Collaboration in Financial Engineering

Battle Count: 35

While focused on credit decisions, the explainable AutoML approach could be adapted for quantitative trading strategies, particularly in risk assessment and decision-making processes

Requires integration of AutoML and XAI techniques, but uses established frameworks like H2O and SHAP

The paper provides detailed information on datasets, preprocessing steps, and AutoML setup, enhancing reproducibility