Credit-risk evaluation is a very challenging and important management science problem in the domain of financial analysis. Many classification methods have been suggested in the literature to tackle this problem. Neural networks, especially, have received a lot of attention because of their...

One of the key decisions financial institutions have to make as part of their daily operations is to decide whether or not to grant a loan to an applicant. With the emergence of large-scale data-storing facilities, huge amounts of data have been stored regarding the repayment behavior of past...

An important objective of data mining is the development of predictive models. Based on a number of observations, a model is constructed that allows the analysts to provide classifications or predictions for new observations. Currently, most research focuses on improving the accuracy or...

Decision trees are a widely used knowledge representation in machine learning. However, one of their main drawbacks is the inherent replication of isomorphic subtrees, as a result of which the produced classifiers might become too large to be comprehensible by the human experts that have to...

The introduction of the Basel II Accord has had a huge impact on financial institutions, allowing them to build credit risk models for three key risk parameters: PD (probability of default), LGD (loss given default) and EAD (exposure at default). Until recently, credit risk research has focused...

Start-ups are crucial in the modern economy as they provide dynamism and growth. Research on the performance of new ventures increasingly investigates initial resources as determinants of success. Initial resources are said to be important because they imprint the firm at start-up, limit its...