We propose an adversarial deep learning model for credit risk modeling. We make use of sophisticated machine learning model's ability to triangulate (i.e., ...
We propose an adversarial deep learning model for credit risk modeling. We make use of sophisticated machine learning model's ability to triangulate.
May 11, 2024 · ICIS. Credit Risk Modeling without Sensitive Features: An Adversarial Deep Learning Model for Fairness and Profit. Hu, Xiyang, Huang, Yan, Li ...
Xiyang Hu, Yan Huang, Beibei Li, Tian Lu (2022). Credit Risk Modeling without Sensitive Features: An Adversarial Deep Learning Model for Fairness and Profit.
Credit Risk Modeling without Sensitive Features: An Adversarial Deep Learning Model for Fairness and Profit. X Hu, Y Huang, B Li, T Lu. International Conference ...
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Which models can be used to build a credit risk model?
"Credit Risk Modeling without Sensitive Features: An Adversarial Deep Learning Model for Fairness and Profit," with Xiyang Hu, Beibei Li and Tian Lu. Page ...
Our track is concerned with research on the design and evaluation of sociotechnical AI-based systems that achieve multi-sided outcomes and are meaningful to ...
Jul 7, 2024 · This framework is tailored for credit risk prediction using real-world financial data, drawing on structural similarities to language by ...
For instance, defining the key qualities of explainability tools and clarifying expectations about how and when lenders should search for fairer alternative.
This research are indications that ensemble models when applied to credit risk modelling are more accurate than traditional single ML algorithms. ... ...