Deep generative models for reject inference in credit scoring

By Rogelio Andrade Mancisidor, Michael C. Kampffmeyer, Kjersti Aas, Robert Jenssen. 2019

57 citations recorded by the source corpus.

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 classification accuracy in credit scoring models by adding reject applications. Our proposed models infer the unknown creditworthiness of the rejected applications by exact enumeration of the two possible outcomes of the loan (default or non-default). The efficient stochastic gradient optimization technique used in deep generative models makes our models suitable for large data sets. Finally, the experiments in this research show that our proposed models perform better than classical and alternative machine learning models for reject inference in credit scoring.

Subfields: Accounting, Artificial Intelligence

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In Artificial Intelligence

1. Alexandra Chouldechova
2. Amanda Coston
3. Ashesh Rambachan
4. Emilio Ferrara

Citations in the corpus

• Characterizing Fairness Over the Set of Good Models Under Selective Labels (2021, methodology): “, rejected applicants) in model construction and evaluation by imputing missing outcomes using augmentation, reweighing or extrapolation-based approaches (Li et al., 2020; Mancisidor et al., 2020).”