  Credit scoring models support loan approval decisions in the financial
services industry. Lenders train these models on data from previously granted
credit applications, where the borrowers' repayment behavior has been observed.
This approach creates sample bias. The scoring model (i.e., classifier) is
trained on accepted cases only. Applying the resulting model to screen credit
applications from the population of all borrowers degrades model performance.
Reject inference comprises techniques to overcome sampling bias through
assigning labels to rejected cases. The paper makes two contributions. First,
we propose a self-learning framework for reject inference. The framework is
geared toward real-world credit scoring requirements through considering
distinct training regimes for iterative labeling and model training. Second, we
introduce a new measure to assess the effectiveness of reject inference
strategies. Our measure leverages domain knowledge to avoid artificial labeling
of rejected cases during strategy evaluation. We demonstrate this approach to
offer a robust and operational assessment of reject inference strategies.
Experiments on a real-world credit scoring data set confirm the superiority of
the adjusted self-learning framework over regular self-learning and previous
reject inference strategies. We also find strong evidence in favor of the
proposed evaluation measure assessing reject inference strategies more
reliably, raising the performance of the eventual credit scoring model.
