[HTML][HTML] Explainable AI for operational research: A defining framework, methods, applications, and a research agenda
The ability to understand and explain the outcomes of data analysis methods, with regard to
aiding decision-making, has become a critical requirement for many applications. For …
aiding decision-making, has become a critical requirement for many applications. For …
[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 …
Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market
Technical analysis indicators are popular tools in financial markets. These tools help
investors to identify buy and sell signals with relatively large errors. The main goal of this …
investors to identify buy and sell signals with relatively large errors. The main goal of this …
[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 …
An explainable federated learning and blockchain-based secure credit modeling method
F Yang, MZ Abedin, P Hajek - European Journal of Operational Research, 2024 - Elsevier
Federated learning has drawn a lot of interest as a powerful technological solution to the
“credit data silo” problem. The interpretability of federated learning is a crucial issue due to …
“credit data silo” problem. The interpretability of federated learning is a crucial issue due to …
Fairness in credit scoring: Assessment, implementation and profit implications
The rise of algorithmic decision-making has spawned much research on fair machine
learning (ML). Financial institutions use ML for building risk scorecards that support a range …
learning (ML). Financial institutions use ML for building risk scorecards that support a range …
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 reinforcement learning with the confusion-matrix-based dynamic reward function for customer credit scoring
Y Wang, Y Jia, Y Tian, J **ao - Expert Systems with Applications, 2022 - Elsevier
Customer credit scoring is a dynamic interactive process. Simply designing the static reward
function for deep reinforcement learning may be difficult to guide an agent to adapt to the …
function for deep reinforcement learning may be difficult to guide an agent to adapt to the …
Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China
Y Liu, M Yang, Y Wang, Y Li, T **ong, A Li - International Review of …, 2022 - Elsevier
Using data from Renrendai and three machine learning algorithms, namely, k-nearest
neighbor, support vector machine, and random forest, we predicted the default probability of …
neighbor, support vector machine, and random forest, we predicted the default probability of …
[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 …