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Machine learning in credit risk : measuring the dilemma between prediction and supervisory cost

Alonso, Andrés; Carbó, José Manuel - 2020

Persistent link: https://www.econbiz.de/10012491781

Accuracy of explanations of machine learning models for credit decisions

Alonso, Andrés; Carbó, José Manuel - 2022

Persistent link: https://www.econbiz.de/10013389619

Analysis of CBDC narrative of central banks using large language models

Alonso, Andrés; Carbó, José Manuel - 2023

Persistent link: https://www.econbiz.de/10014366178

Understanding the Performance of Machine Learning Models to Predict Credit Default : A Novel Approach for Supervisory Evaluation

Alonso, Andrés; Carbó, José Manuel - 2021

regression, Classification And Regression Tree (CART), Random Forest, XGBoost and Deep Neural Networks. Following the process … predictive power, both in classification and calibration. Running a simulation exercise for different sample sizes and number of … model advantage. Despite the fact that ML models outperforms Logit both in classification and in calibration, more complex …

Persistent link: https://www.econbiz.de/10013247550

Understanding the performance of machine learning models to predict credit default : a novel approach for supervisory evaluation

Alonso, Andrés; Carbó, José Manuel - 2021

Persistent link: https://www.econbiz.de/10012489537

Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction

Alonso, Andrés; Carbó Martínez, José Manuel - In: Financial innovation : FIN 8 (2022), pp. 1-35

Implementing new machine learning (ML) algorithms for credit default prediction is associated with better predictive performance; however, it also generates new model risks, particularly concerning the supervisory validation process. Recent industry surveys often mention that uncertainty about how...

Persistent link: https://www.econbiz.de/10013332386

Accuracy of Explanations of Machine Learning Models for Credit Decision

Alonso, Andrés; Carbó, José Manuel - 2022

One of the biggest challenges for the application of machine learning (ML) models in finance is how to explain their results. In recent years, different interpretability techniques have appeared to assist in this task, although their usefulness is still a matter of debate. In this article we...

Persistent link: https://www.econbiz.de/10013405270

Can machine learning models save capital for banks? : evidence from a Spanish credit portfolio

Alonso, Andrés; Carbó, José Manuel - In: International review of financial analysis 84 (2022), pp. 1-12

Persistent link: https://www.econbiz.de/10013472817

Machine learning methods in climate finance : a systematic review

Alonso, Andrés; Carbó, José Manuel; Marqués, José … - 2023

Persistent link: https://www.econbiz.de/10014278712

Machine Learning Methods in Climate Finance : A Systematic Review

Alonso, Andrés; Carbó, José Manuel; Marqués, J. Manuel - 2023

Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as...

Persistent link: https://www.econbiz.de/10014261048