The need for controlling and effectively managing credit risk has led financial institutions to
excel in improving techniques designed for this purpose, resulting in the development 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 …

In recent years, research has found that in many credit risk evaluation domains, deep
learning is superior to traditional machine learning methods and classifier ensembles …

Financial time series (FTS) are nonlinear, dynamic and chaotic. The search for models to
facilitate FTS forecasting has been highly pursued for decades. Despite major related …

In the current dynamic environment, organizations are exposed to many risks from different
directions. Therefore, this study using the theoretical lens explored the effect of enterprise …

In the last years, the application of artificial intelligence methods on credit risk assessment
has meant an improvement over classic methods. Small improvements in the systems about …

Banks take great care when dealing with customer loans to avoid any improper decisions
that can lead to loss of opportunity or financial losses. Regarding this, researchers have …

There is often a significant class imbalance in credit scoring datasets, mainly in portfolios of
secured loans such as mortgage loans. A class imbalance occurs when the number of non …

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 …

We predict mortgage default by applying convolutional neural networks to consumer
transaction data. For each consumer we have the balances of the checking account, savings …