90% Accuracy: The Best Machine Learning Models for Credit Risk Prediction

In the realm of financial analytics, the quest for the most effective machine learning models for credit risk prediction is ongoing. Traditional models like logistic regression and decision trees have been staples, but newer methods such as deep learning and ensemble techniques are gaining traction. A study comparing these models found that:

Logistic Regression offers high interpretability but often lacks in accuracy compared to more advanced models.
Decision Trees provide a balance between accuracy and interpretability, with an accuracy rate around 75%.
Deep Learning models, particularly neural networks, can achieve up to 90% accuracy but are less interpretable and require significant computational resources.
Ensemble Techniques like Random Forests or Gradient Boosting Machines (GBM) combine multiple models to improve accuracy, often reaching 85-90% accuracy while maintaining some level of interpretability.

The choice of model often depends on the trade-off between accuracy, interpretability, and compliance with financial regulations.

Source: stackoverflow.com