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 novel federated learning approach with knowledge transfer for credit scoring
Z Wang, J Xiao, L Wang, J Yao - Decision Support Systems, 2024 - Elsevier
The expanding availability of data in the financial sector promises to take the performance of
machine learning models to a new level. However, given the high business value and …
machine learning models to a new level. However, given the high business value and …
Ensemble methods in customer churn prediction: A comparative analysis of the state-of-the-art
M Bogaert, L Delaere - Mathematics, 2023 - mdpi.com
In the past several single classifiers, homogeneous and heterogeneous ensembles have
been proposed to detect the customers who are most likely to churn. Despite the popularity …
been proposed to detect the customers who are most likely to churn. Despite the popularity …
Assessing financial distress of SMEs through event propagation: An adaptive interpretable graph contrastive learning model
Accurate assessment of financial distress of SMEs is critical as it has strong implications for
various stakeholders to understand the firm's financial health. Recent studies start to …
various stakeholders to understand the firm's financial health. Recent studies start to …
Efficient fraud detection using deep boosting decision trees
B Xu, Y Wang, X Liao, K Wang - Decision Support Systems, 2023 - Elsevier
Fraud detection is to identify, monitor, and prevent potentially fraudulent activities from
complex data. The recent development and success in AI, especially machine learning …
complex data. The recent development and success in AI, especially machine learning …
RDGSL: Dynamic Graph Representation Learning with Structure Learning
Temporal Graph Networks (TGNs) have shown remarkable performance in learning
representation for continuous-time dynamic graphs. However, real-world dynamic graphs …
representation for continuous-time dynamic graphs. However, real-world dynamic graphs …
RaKShA: A Trusted Explainable LSTM Model to Classify Fraud Patterns on Credit Card Transactions
Credit card (CC) fraud has been a persistent problem and has affected financial
organizations. Traditional machine learning (ML) algorithms are ineffective owing to the …
organizations. Traditional machine learning (ML) algorithms are ineffective owing to the …
A relative granular ratio-based outlier detection method in heterogeneous data
Outlier detection is the discovery of some objects that are significantly different from many
objects in data, and it is widely used in important fields. Most existing methods are based on …
objects in data, and it is widely used in important fields. Most existing methods are based on …
Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions
ID Mienye, N Jere - IEEE Access, 2024 - ieeexplore.ieee.org
Deep learning (DL), a branch of machine learning (ML), is the core technology in today's
technological advancements and innovations. Deep learning-based approaches are the …
technological advancements and innovations. Deep learning-based approaches are the …
A spatial–temporal graph-based AI model for truck loan default prediction using large-scale GPS trajectory data
L Chen, S Ma, C Li, Y Yang, W Wei, R Cui - Transportation Research Part E …, 2024 - Elsevier
With the increasing uncertainties in freight transportation, truck loans are playing a crucial
role in the stability and development of the logistics industry. A pivotal problem to truck loan …
role in the stability and development of the logistics industry. A pivotal problem to truck loan …