Artificial intelligence in business: State of the art and future research agenda
This study provides an overview of state-of-the-art research on Artificial Intelligence in the
business context and proposes an agenda for future research. First, by analyzing 404 …
business context and proposes an agenda for future research. First, by analyzing 404 …
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 …
A comparative performance analysis of data resampling methods on imbalance medical data
Medical datasets are usually imbalanced, where negative cases severely outnumber
positive cases. Therefore, it is essential to deal with this data skew problem when training …
positive cases. Therefore, it is essential to deal with this data skew problem when training …
Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning
J Engelmann, S Lessmann - Expert Systems with Applications, 2021 - Elsevier
Class imbalance impedes the predictive performance of classification models. Popular
countermeasures include oversampling minority class cases by creating synthetic examples …
countermeasures include oversampling minority class cases by creating synthetic examples …
[HTML][HTML] Interpretable machine learning for imbalanced credit scoring datasets
The class imbalance problem is common in the credit scoring domain, as the number of
defaulters is usually much less than the number of non-defaulters. To date, research on …
defaulters is usually much less than the number of non-defaulters. To date, research on …
A novel ensemble method for credit scoring: Adaption of different imbalance ratios
H He, W Zhang, S Zhang - Expert Systems with Applications, 2018 - Elsevier
In the past few decades, credit scoring has become an increasing concern for financial
institutions and is currently a popular topic of research. This study aims to generate a novel …
institutions and is currently a popular topic of research. This study aims to generate a novel …
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 …
Class weights random forest algorithm for processing class imbalanced medical data
M Zhu, J Xia, X Jin, M Yan, G Cai, J Yan, G Ning - IEEE Access, 2018 - ieeexplore.ieee.org
The classification in class imbalanced data has drawn significant interest in medical
application. Most existing methods are prone to categorize the samples into the majority …
application. Most existing methods are prone to categorize the samples into the majority …
Investigation and improvement of multi-layer perceptron neural networks for credit scoring
Abstract Multi-Layer Perceptron (MLP) neural networks are widely used in automatic credit
scoring systems with high accuracy and efficiency. This paper presents a higher accuracy …
scoring systems with high accuracy and efficiency. This paper presents a higher accuracy …