[HTML][HTML] Operational research and artificial intelligence methods in banking
Banking is a popular topic for empirical and methodological research that applies
operational research (OR) and artificial intelligence (AI) methods. This article provides a …
operational research (OR) and artificial intelligence (AI) methods. This article provides a …
Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods
Credit risk assessment is a crucial element in credit risk management. With the extensive
research on consumer credit risk assessment in recent decades, the abundance of literature …
research on consumer credit risk assessment in recent decades, the abundance of literature …
Deep learning for credit scoring: Do or don't?
Develo** accurate analytical credit scoring models has become a major focus for financial
institutions. For this purpose, numerous classification algorithms have been proposed for …
institutions. For this purpose, numerous classification algorithms have been proposed for …
HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture
Credit card transaction fraud costs billions of dollars to card issuers every year. A well-
developed fraud detection system with a state-of-the-art fraud detection model is regarded …
developed fraud detection system with a state-of-the-art fraud detection model is regarded …
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 …
Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending
Online peer-to-peer (P2P) lending is a new form of loans. Different from traditional banks,
lenders provide loans to borrowers directly through P2P platforms. Since many P2P loans …
lenders provide loans to borrowers directly through P2P platforms. Since many P2P loans …
XGBoost optimized by adaptive particle swarm optimization for credit scoring
C Qin, Y Zhang, F Bao, C Zhang… - Mathematical Problems …, 2021 - Wiley Online Library
Personal credit scoring is a challenging issue. In recent years, research has shown that
machine learning has satisfactory performance in credit scoring. Because of the advantages …
machine learning has satisfactory performance in credit scoring. Because of the advantages …
[HTML][HTML] Credit scoring methods: Latest trends and points to consider
A Markov, Z Seleznyova, V Lapshin - The Journal of Finance and Data …, 2022 - Elsevier
Credit risk is the most significant risk by impact for any bank and financial institution.
Accurate credit risk assessment affects an organisation's balance sheet and income …
Accurate credit risk assessment affects an organisation's balance sheet and income …
[HTML][HTML] A mathematical programming approach to SVM-based classification with label noise
In this paper we propose novel methodologies to optimally construct Support Vector
Machine-based classifiers that take into account that label noise occur in the training …
Machine-based classifiers that take into account that label noise occur in the training …
What should lenders be more concerned about? Develo** a profit-driven loan default prediction model
L Zhang, J Wang, Z Liu - Expert Systems with Applications, 2023 - Elsevier
Reliable and effective loan default risk prediction can help regulators and lenders effectively
identify risky loan applicants and develop proactive and timely response measures to …
identify risky loan applicants and develop proactive and timely response measures to …