In a situation where the number of non-performing loans (NPLs) increases, lenders may raise interest rates to compensate for potential losses, and the amount of credit granted in the market may decrease, leading to credit rationing. Such actions may become vital based on their potential...

In the present study, we model the migration process and the changes in the market environment. The migration process is being modeled as an F -inhomogeneous semi-Markov process with fuzzy states. The evolution of the migration process takes place within a stochastic market environment with...

This chapter presents evidence from empirical analysis which highlights the importance of other credit risks (CRs), in addition to concerns solely based on debt sustainability thresholds. Core CRs identified in the chapter include mismanagement of borrowed funds in the course of execution of...

Accurately predicting credit risk is vital for banks to avoid bankruptcy. Furthermore, there is tremendous pressure to successfully manage credit risk while meeting changing consumer expectations and regulatory norms, yet there is no practical framework in Vietnam for applying advanced machine...

This study examines the impact of corruption on credit risk in Southeast Asian commercial banks by using machine learning models to predict non-performing loans (NPLs) based on the Corruption Perception Index (CPI). Analyzing data from 70 banks over a decade, it employs Decision Tree, Random...

We attempt to assess and explain the credit risk premium of peer-to-peer (P2P) loans for small business purposes. Our evidence shows that P2P borrowers pay significantly higher interest rates for small business financing purposes than for other purposes, controlling for loan amount and...