Should Bank Stress Tests Be Fair?
"We argue that simply pooling data across banks treats banks equally but is subject to two deficiencies: it may distort the impact of...
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15 juil. 2022
Constructing Early Warning Indicators for Banks Using Machine Learning Models
"This research contributes to bank liquidity risk management by employing supervised machine learning models to provide banks with early...
1 juin 2022
A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groups
"... we introduce a novel methodology to model rating transitions with a stochastic process. To introduce stochastic processes, whose...
23 mai 2022
Deep Learning in Business Analytics: A Clash of Expectations and Reality
"... DL [deep learning] does not outperform traditional ML [machine learning] models in the case of structured datasets with fixed-length...
17 mai 2022
Mack-Net model: Blending Mack's model with Recurrent Neural Networks
"... blending traditional reserving models with deep and machine learning techniques leads to a more accurate assessment of general...
21 avr. 2022
Explainable Artificial Intelligence (Xai) in Insurance: A Systematic Review
"Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and...
8 févr. 2022
Corporate Controversies and Financial Stability in the Non-Life Insurance Industry
"Neural networks are suggested for learning a map from d -dimensional samples with any underlying dependence structure to multivariate...
8 févr. 2022
Dependence model assessment and selection with DecoupleNets
"Neural networks are suggested for learning a map from d' dimensional samples with any underlying dependence structure to multivariate...
7 févr. 2022
Forecasting Social Unrest: A Machine Learning Approach
"We produce a social unrest risk index for 125 countries covering a period of 1996 to 2020. The risk of social unrest is based on the...
28 déc. 2021
Reinforcement Learning with Dynamic Convex Risk Measures
"We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems using model-free reinforcement...
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