requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a … predicting financial risk, which is essential for both financial companies and their customers. In addition, results show that …

In terms of regulatory and economic capital, credit risk is the most significant risk faced by banks. We implement a … credit risk model - based on publicly available information . with the aim of developing a tool to monitor credit risk in a … sample of large and complex banking groups (LCBGs) in the EU. The results indicate varying credit risk profiles across these …

We propose a simple risk-adjusted linear approximation to solve a large class of dynamic models with time-varying and … non-Gaussian risk. Our approach generalizes lognormal affine approximations commonly used in the macro-finance literature … risk-adjusted linearizations. We provide a formal foundation for approximation methods that remained so far heuristic, and …

We propose a simple risk-adjusted linear approximation to solve a large class of dynamic models with time-varying and … non-Gaussian risk. Our approach generalizes lognormal affine approximations commonly used in the macro-finance literature … risk-adjusted linearizations. We provide a formal foundation for approximation methods that remained so far heuristic, and …

is used to solve it and tested on a real data from a South Korean online lending company. In our problem setting, the … terms of acceptance rate at the same risk level …

We study the optimal loan securitization policy of a commercial bank which is mainly engaged in lending activities. For …