So we were able to bring this to our model risk and credit risk governance, and apply the existing governance to that. For instance, in some of our stress-testing models, we were able to apply ...

This is true of both the Lando book and its closest peers Schönbucher (Credit derivatives pricing models, John Wiley & Sons, 2003), Bluhm, Overbeck and Wagner (An introduction to credit risk modeling, ...

The machine-learning model allows for a more accurate pricing of default risk and thus for a greater supply of credit. But the benefits in cheaper mortgages go disproportionately to white borrowers.

Raquel Florez-Lopez, Juan Manuel Ramon-Jeronimo, Modelling credit risk with scarce default data: on the suitability of cooperative bootstrapped strategies for small low-default portfolios, The Journal ...

The key function of banks in the real world is endogenously creating (inside) money. But they do so facing solvency, liquidity and maturity risks and being subject to regulatory and demand constraints ...

This paper provides an alternative approach to Duffie and Lando [Econometrica 69 (2001) 633-664] for obtaining a reduced form credit risk model from a structural model. Duffie and Lando obtain a ...

Ford Credit's consumer credit-risk model will include nontraditional data and machine learning to give the lender a better view of a car buyer's likelihood to make payments.

The objective of this paper is to present an integrated tool suite for IFRS 9- and CECL-compatible estimation in top-down solvency stress tests. The tool suite serves as an illustration for ...

Credit risk models, which assist in credit appraisal, borrower scoring, pricing, and risk management, are inherently uncertain due to their reliance on assumptions that may not always hold true in ...