Statistical and machine learning models in credit scoring: A systematic literature survey
In practice, as a well-known statistical method, the logistic regression model is used to
evaluate the credit-worthiness of borrowers due to its simplicity and transparency in …
evaluate the credit-worthiness of borrowers due to its simplicity and transparency in …
Machine learning-driven credit risk: a systemic review
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 …
statistical methods and manual auditing. Recent advances in financial artificial intelligence …
Machine learning techniques for credit risk evaluation: a systematic literature review
Credit risk is the risk of financial loss when a borrower fails to meet the financial commitment.
While there are many factors that constitute credit risk, due diligence while giving loan (credit …
While there are many factors that constitute credit risk, due diligence while giving loan (credit …
A comparative study on base classifiers in ensemble methods for credit scoring
J Abellán, JG Castellano - Expert systems with applications, 2017 - Elsevier
In the last years, the application of artificial intelligence methods on credit risk assessment
has meant an improvement over classic methods. Small improvements in the systems about …
has meant an improvement over classic methods. Small improvements in the systems about …
An explainable AI decision-support-system to automate loan underwriting
Widespread adoption of automated decision making by artificial intelligence (AI) is
witnessed due to specular advances in computation power and improvements in …
witnessed due to specular advances in computation power and improvements in …
Classification methods applied to credit scoring: Systematic review and overall comparison
The need for controlling and effectively managing credit risk has led financial institutions to
excel in improving techniques designed for this purpose, resulting in the development of …
excel in improving techniques designed for this purpose, resulting in the development of …
Two-stage consumer credit risk modelling using heterogeneous ensemble learning
M Papouskova, P Hajek - Decision support systems, 2019 - Elsevier
Modelling consumer credit risk is a crucial task for banks and non-bank financial institutions
to support decision-making on granting loans. To model the overall credit risk of a consumer …
to support decision-making on granting loans. To model the overall credit risk of a consumer …
A novel heterogeneous ensemble credit scoring model based on bstacking approach
Y Xia, C Liu, B Da, F Xie - Expert Systems with Applications, 2018 - Elsevier
In recent years, credit scoring has become an efficient tool that allows financial institutions to
differentiate their potential default borrowers. Accordingly, researchers have developed a …
differentiate their potential default borrowers. Accordingly, researchers have developed a …
[图书][B] Credit scoring and its applications
L Thomas, J Crook, D Edelman - 2017 - SIAM
Credit Scoring and Its Applications, Second Edition : Back Matter Page 1 Bibliography [1]
Acharya, VV, Bharath, ST, and Srinivasan, A. (2007) Does industry-wide distress affect …
Acharya, VV, Bharath, ST, and Srinivasan, A. (2007) Does industry-wide distress affect …
A new hybrid ensemble credit scoring model based on classifiers consensus system approach
M Ala'raj, MF Abbod - Expert systems with applications, 2016 - Elsevier
During the last few years there has been marked attention towards hybrid and ensemble
systems development, having proved their ability to be more accurate than single classifier …
systems development, having proved their ability to be more accurate than single classifier …