We discuss how to assess the performance for credit scores under the assumption that for credit data only a part of the defaults and nondefaults is observed. The paper introduces a criterion that is based on the difference of the score distributions under default and nondefault. We show how to...

The literature on corporate credit risk modeling for privately-held firms is scarce. Although firms with unlisted equity or debt represent a significant fraction of the corporate sector worldwide, research in this area has been hampered by the unavailability of public data. This study is an...

This paper examines racial and ethnic differences in high cost mortgage lending in seven diverse metropolitan areas from 2004-2007. Even after controlling for credit score and other key risk factors, African-American and Hispanic home buyers are 105 and 78 percent more likely to have high cost...

The transformation of credit scores into probabilities of default plays an important role in credit risk estimation. The linear logistic regression has developed into a standard calibration approach in the banking sector. With the advent of machine learning techniques in the discriminatory phase...

Using firm-level data from surveys and financial statements, this paper presents an analysis of credit standards, capital allocation and financial conditions of non-financial enterprises in Denmark since the beginning of the financial crisis. The analysis indicates that low interest rates and...

We discuss how to assess the performance for credit scores under the assumption that for credit data only a part of the defaults and nondefaults is observed. The paper introduces a criterion that is based on the difference of the score distributions under default and nondefault. We show how to...

The transformation of credit scores into probabilities of default plays an important role in credit risk estimation. The linear logistic regression has developed into a standard calibration approach in the banking sector. With the advent of machine learning techniques in the discriminatory phase...

FinTech lending—known for using big data and advanced technologies—promised to break away from the traditional credit scoring and pricing models. Using a comprehensive dataset of FinTech personal loans, our study shows that loan rates continue to rely heavily on conventional credit scores,...

This paper examines how high cost mortgage lending varies by race and ethnicity. It uses a unique panel data that matches a representative sample of mortgages in seven large metropolitan markets between 2004 and 2008 to public records of housing transactions and proprietary credit reporting...

This paper examines how high cost mortgage lending varies by race and ethnicity. It uses a unique panel data that matches a representative sample of mortgages in seven large metropolitan markets between 2004 and 2008 to public records of housing transactions and proprietary credit reporting...