Credit risk is the most significant risk by impact for any bank and financial institution.
Accurate credit risk assessment affects an organisation's balance sheet and income …

The expanding availability of data in the financial sector promises to take the performance of
machine learning models to a new level. However, given the high business value and …

With society's wide-scale adoption of information technology, significant information about
borrowers is distributed across various parties, information that can be jointly used to …

This paper investigates the performance of thirteen methods for modelling and predicting
mortgage early delinquency probabilities. These models include variants of logit models …

Accurate credit risk prediction effectively supports decision makings and risk prevention in
quantitative management. The general paradigm of previous works usually conducts …

Booming online consumer lending encounters high credit risk and thus needs well-designed
credit scoring models. As a supplementary data source, multilevel macroeconomic variables …

This paper explores the potential of 19 machine learning techniques to model and forecasts
the risk of delinquency on mortgage loans. These techniques include variants of artificial …

Credit scoring models are usually developed using logistic regression. For several reasons,
professionals of this area frequently categorize the quantitative covariates before using them …

Credit rating is an essential method for credit risk management and has been applied in
many fields. However, in the process of credit rating, the risk attitude of credit rating manager …

Internet consumer credit services are defined as the provision of consumer credit services
through internet platforms. While these services have benefited the public, they also present …