Contagious loan default

When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications

When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications

The authors present empirical evidence that borrowers, consciously or not, leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan. This textual information has a substantial and significant ability to predict whether borrowers will pay back the loan over and beyond the financial and demographic variables commonly used in models pr...

Laying off Credit Risk: Loan Sales versus Credit Default Swaps∗

After making a loan, a bank finds out if the loan needs contract enforcement (“monitoring”); it also decides whether to lay off credit risk in order to release costly capital. A bank can lay off credit risk by either selling the loan or by buying insurance through a credit default swap (CDS). With a CDS, the originating bank retains the loan’s control rights but no longer has an incentive to mo...

Loan Default Prediction on Large Imbalanced Data Using Random Forests

In this paper, we propose an improved random forest algorithm which allocates weights to decision trees in the forest during tree aggregation for prediction and their weights are easily calculated based on out-of-bag errors in training. Experiments results show that our proposed algorithm beats the original random forest and other popular classification algorithms such as SVM, KNN and C4.5 in t...

Mortgage Lending Discrimination and Racial Differences in Loan Default

This article analyzes the use of racial differences in loan default to test for mortgage lending discrimination—an approach whose underlying theory has not been carefully explored. The loan approval process creates a selected sample of loans. If mortgage lenders hold minorities to a higher standard, the minority sample should have greater selection bias than the majority sample and so should ex...