Leveraging Uncertainty for Credit Risk Estimation and Reliable Predictions in lending decision making
S. K. Kar, Akash Mondal · 2023
As the adoption of point prediction models, such as deep neural networks and Gradient Boosting methods, continues to grow in critical decision-making systems, ensuring the reliability of their inferences has become a paramount concern. These deterministic models often exhibit excessively high confidence even on out-of-distribution datasets, leading to potentially costly errors in essential applications like lending decisions and credit risk estimations. Hence, accurate uncertainty quantification is essential for practical and reliable applications.