Under the new Basel bank capital framework, a bank must group its retail exposures into multiple segments with homogeneous risk characteristics. The U.S. regulatory agencies believe that a bank may use the internal models, including the loan-level risk parameter estimates such as PD and LGD, to...

The recovery rate on defaulted corporate bonds has a time-varying distribution. We propose machine learning approaches for intertemporal analysis of U.S. corporate bonds' recovery rates with a large number of predictors. The most informative macroeconomic variables are selected from a broad...

Under the new Basel bank capital framework, each bank must group its retail exposures into multiple segments with homogeneous risk characteristics. The U.S. regulatory agencies believe that each bank may use its internal risk models for the loan-level risk parameter estimates such as probability...

Default correlation is a key driver of credit risk. In the Basel regulatory framework it is measured by the asset value correlation parameter. Though past studies suggest that the parameter is over-calibrated for mortgages — generally the largest asset class on banks' balance sheets — they...

Banks’ credit scoring models are required by financial authorities to be explainable. This paper proposes an explainable artificial intelligence (XAI) model for predicting credit default on a unique dataset of unsecured consumer loans provided by a Norwegian bank. We combined a LightGBM model...

While previous academic research highlights the potential of machine learning and big data for predicting corporate bond recovery rates, the operations management challenge is to identify the relevant predictive variables and the appropriate model. In this paper, we use meta-learning to combine...