Banking is a popular topic for empirical and methodological research that applies
operational research (OR) and artificial intelligence (AI) methods. This article provides a …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …