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Explainable AI in Credit Risk Management [PDF]
Social Science Research Network, 2021 Artificial Intelligence (AI) has created the single biggest technology revolution the world has ever seen. For the finance sector, it provides great opportunities to enhance customer experience, democratize financial services, ensure consumer protection ...
Branka Hadji Misheva +4 more
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Machine Learning for Credit Risk Prediction: A Systematic Literature Review
International Conference on Data Technologies and Applications, 2023 In this systematic review of the literature on using Machine Learning (ML) for credit risk prediction, we raise the need for financial institutions to use Artificial Intelligence (AI) and ML to assess credit risk, analyzing large volumes of information ...
J. Noriega +2 more
semanticscholar +1 more source
Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach
Journal of Risk and Financial Management, 2023 Financial institutions and regulators increasingly rely on large-scale data analysis, particularly machine learning, for credit decisions. This paper assesses ten machine learning algorithms using a dataset of over 2.5 million observations from a ...
Nicolas Suhadolnik +2 more
semanticscholar +1 more source
Journal of Risk and Financial Management, 2023 In recent years, after the global financial crisis, the issue of credit risk management has received increased attention from international regulators. Credit risk management frameworks are often not sufficiently integrated within the organization, there
Tribhuwan Kumar Bhatt +3 more
semanticscholar +1 more source
Credit Ratings and Credit Risk [PDF]
SSRN Electronic Journal, 2012 This paper investigates the information in corporate credit ratings. We examine the extent to which firms' credit ratings measure raw probability of default as opposed to systematic risk of default, a firm's tendency to default in bad times. We find that credit ratings are dominated as predictors of corporate failure by a simple model based on publicly
Jens Hilscher, Mungo Wilson
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Machine learning-driven credit risk: a systemic review
Neural computing & applications (Print), 2022 Credit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk ...
Si Shi +4 more
semanticscholar +1 more source
International Journal of Financial Studies, 2021 In banking and finance, credit risk is among the important topics because the process of issuing a loan requires a lot of attention to assessing the possibilities of getting the loaned money back.
David Mhlanga
semanticscholar +1 more source
Model risk on credit risk [PDF]
Risk and Decision Analysis, 2016 This paper develops the Jungle model in a credit portfolio framework. The Jungle model is able to model credit contagion, produce doubly-peaked probability distributions for the total default loss and endogenously generate quasi phase transitions, potentially leading to systemic credit events which happen unexpectedly and without an underlying single ...
J. Molins, Eduard Vives
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SSRN Electronic Journal, 2020 What is the impact of granular credit risk on banks and on the economy? We provide the first causal identification of single-name counterparty exposure risk in bank portfolios by applying a new empirical approach on an administrative matched bank-firm dataset from Norway. Exploiting the fat tail properties of the loan share distribution we use a Gabaix
Galaasen, Sigurd +3 more
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Managing Credit Risk with Credit Derivatives [PDF]
SSRN Electronic Journal, 2005 Credit risk is one of the most important forms of risk faced by national and international banks as financial intermediaries. Managing this kind of risk through selecting and monitoring corporate and sovereign borrowers and through creating a diversified loan portfolio has always been one of the predominant challenges in bank management. The aim of our
UDO BROLL +2 more
openaire +2 more sources