Deep generative models for reject inference in credit scoring

  title={Deep generative models for reject inference in credit scoring},
  author={R. A. Mancisidor and Michael C. Kampffmeyer and K. Aas and R. Jenssen},
  journal={Knowl. Based Syst.},
Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-supervised Bayesian models for reject inference in credit scoring, in which we model the data generating process to be dependent on a Gaussian mixture. The goal is to improve the… Expand
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