Models evaluating credit applicants rely on payment performance data, which is only available for accepted applicants. This sampling limitation could lead to biased parameter estimates. We use a nationally representative sample of credit bureau records to examine sample selection bias in account...

While credit rating agencies are of central importance to credit markets, it is still unclear how credit ratings decisions are being made. Are credit rating quantitatively hard-wired? Or, do ratings represent a more holistic approach to the evaluation of creditworthiness? I evaluate the...

Our analysis uses a different, unique proprietary dataset that features three frequently used credit scores for each individual. Compared with the dataset used in the CFPB report, this dataset includes more recent time periods and provides a longer historical perspective of credit score comparisons

Small- and medium-sized enterprises (SMEs) have a crucial influence on the economic development of every nation, but access to formal finance remains a barrier. Similarly, financial institutions encounter challenges in the assessment of SMEs' creditworthiness for the provision of financing....

In credit markets, screening algorithms discriminate between good-type and bad-type borrowers. This is their raison d’être. However, by doing so, they also often discriminate between individuals sharing a protected attribute (e.g. gender, age, race) and the rest of the population. In this...