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 methods in smart lighting toward achieving user comfort: a survey
Smart lighting has become a universal smart product solution, with global revenues of up to
US 5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) …
US 5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) …
A neural network ensemble with feature engineering for improved credit card fraud detection
Recent advancements in electronic commerce and communication systems have
significantly increased the use of credit cards for both online and regular transactions …
significantly increased the use of credit cards for both online and regular transactions …
A two-stage hybrid credit risk prediction model based on XGBoost and graph-based deep neural network
J Liu, S Zhang, H Fan - Expert Systems with Applications, 2022 - Elsevier
The credit risk prediction technique is an indispensable financial tool for measuring the
default probability of credit applicants. With the rapid development of machine learning and …
default probability of credit applicants. With the rapid development of machine learning and …
Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level
The groundwater resources are the essential sources for irrigation and agriculture
management. Forecasting groundwater levels (GWL) for the current and future periods is an …
management. Forecasting groundwater levels (GWL) for the current and future periods is an …
A deep learning ensemble with data resampling for credit card fraud detection
Credit cards play an essential role in today's digital economy, and their usage has recently
grown tremendously, accompanied by a corresponding increase in credit card fraud …
grown tremendously, accompanied by a corresponding increase in credit card fraud …
A machine learning approach combining expert knowledge with genetic algorithms in feature selection for credit risk assessment
PZ Lappas, AN Yannacopoulos - Applied Soft Computing, 2021 - Elsevier
Most credit scoring algorithms are designed with the assumption to be executed in an
environment characterized by an automatic processing of credit applications, without …
environment characterized by an automatic processing of credit applications, without …
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 ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in …
G Yao, X Hu, G Wang - Expert Systems with Applications, 2022 - Elsevier
Enterprise credit risk prediction in the supply chain context is an important step for decision
making and early credit crisis warnings. Improving the prediction performance of this task is …
making and early credit crisis warnings. Improving the prediction performance of this task is …
An improved and random synthetic minority oversampling technique for imbalanced data
G Wei, W Mu, Y Song, J Dou - Knowledge-based systems, 2022 - Elsevier
Imbalanced data learning has become a major challenge in data mining and machine
learning. Oversampling is an effective way to re-achieve the balance by generating new …
learning. Oversampling is an effective way to re-achieve the balance by generating new …