During recent years, the empirical research on corruption has grown considerably. Possible links between government corruption and terrorism have attracted an increasing interest in this research field. Most of the existing literature discusses the topic from a socio-economical perspective and...

Insurance companies sell protection to policy holders against many types of risks: property damage or loss, health and casualties, financial losses, etc. In return for this risk protection, insurance companies receive a premium from the policy holder, which is used to cover expenses and the...

Insurance companies sell protection to policy holders against many types of risks: property damage or loss, health and casualties, financial losses, etc. In return for this risk protection, insurance companies receive a premium from the policy holder, which is used to cover expenses and the...

Support vector machines (SVMs) are currently state-of-the-art for the classification task and, generally speaking, exhibit good predictive performance due to their ability to model nonlinearities. However, their strength is also their main weakness, as the generated nonlinear models are...

The use of linear error correction models based on stationarity and cointegration analysis, typically estimated with least squares regression, is a common technique for financial time series prediction. In this paper, the same formulation is extended to a nonlinear error correction model using...

Table of contents Neo-classical reengeneering: Returning to the promise of process in the post-Internet economy M. De Kegel and M. McDonald Towards an integrative framework for software architecture R. Maes and G. Dedene Component based development. From dinosaurs to small, adaptive,...

Data mining involves extracting interesting patterns from data and can be found at the heart of operational research (OR), as its aim is to create and enhance decision support systems. Even in the early days, some data mining approaches relied on traditional OR methods such as linear programming...

Valuing customers is a central issue for any commercial activity. The customer lifetime value (CLV) is the discounted value of the future profits that this customer yields to the company. In order to compute the CLV, one needs to predict the future number of transactions a customer will make and...

Companies' interest in customer relationship modelling and key issues such as customer lifetime value and churn has substantially increased over the years. However, the complexity of building, interpreting and applying these models creates obstacles for their implementation. The main...

Traditionally, credit scoring aimed at distinguishing good payers from bad payers at the time of the application. The timing when customers default is also interesting to investigate since it can provide the bank with the ability to do profit scoring. Analysing when customers default is...