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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
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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
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
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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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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RESTRUCTURING COUNTERPARTY CREDIT RISK [PDF]
International Journal of Theoretical and Applied Finance, 2011 We introduce an innovative theoretical framework for the valuation and replication of derivative transactions between defaultable entities based on the principle of arbitrage freedom. Our framework extends the traditional formulations based on credit and debit valuation adjustments (CVA and DVA).
Albanese, Claudio +2 more
openaire +9 more sources
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
Credit risk assessment mechanism of personal auto loan based on PSO-XGBoost Model
Complex & Intelligent Systems, 2022 As online P2P loans in automotive financing grows, there is a need to manage and control the credit risk of the personal auto loans. In this paper, the personal auto loans data sets on the Kaggle platform are used on a machine learning based credit risk ...
Congjun Rao, Ying Liu, Mark Goh
semanticscholar +1 more source
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