D. Van den Poel

Neural network survival analysis for personal loan data

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 typically tackled using survival analysis. In this paper, we discuss and contrast statistical and neural network approaches for survival analysis. Compared to the proportional hazards model, neural networks may offer an interesting alternative because of their universal approximation property and the fact that no baseline hazard assumption is needed. Several neural network survival analysis models are discussed and evaluated according to their way of dealing with censored observations, time-varying inputs, the monotonicity of the generated survival curves and their scalability. In the experimental part, we contrast the performance of a neural network survival analysis model with that of the proportional hazards model for predicting both loan default and early repayment using data from a UK financial institution.

Knowledge Discovery In A Direct Marketing Case Using Least Squares Support Vector Machines

We study the problem of repeat‐purchase modeling in a direct marketing setting using Belgian data. More specifically, we investigate the detection and qualification of the most relevant explanatory variables for predicting purchase incidence. The analysis is based on a wrapped form of input selection using a sensitivity based pruning heuristic to guide a greedy, stepwise, and backward traversal of the input space. For this purpose, we make use of a powerful and promising least squares support vector machine (LS‐SVM) classifier formulation. This study extends beyond the standard recency frequency monetary (RFM) modeling semantics in two ways: (1) by including alternative operationalizations of the RFM variables, and (2) by adding several other (non‐RFM) predictors. Results indicate that elimination of redundant/irrelevant inputs allows significant reduction of model complexity. The empirical findings also highlight the importance of frequency and monetary variables, while the recency variable category seems to be of somewhat lesser importance to the case at hand. Results also point to the added value of including non‐RFM variables for improving customer profiling. More specifically, customer/company interaction, measured using indicators of information requests and complaints, and merchandise returns provide additional predictive power to purchase incidence modeling for database marketing. © 2001 John Wiley & Sons, Inc.

The impact of sample bias on consumer credit scoring performance and profitability

This article seeks to gain insight into the influence of sample bias in a consumer credit scoring model. In earlier research, sample bias has been suggested to pose a sizeable threat to predictive performance and profitability due to its implications on either population drainage or biased estimates. Contrary to previous—mainly theoretical—research on sample bias, the unique features of the data set used in this study provide the opportunity to investigate the issue in an empirical setting. Based on the data of a mail-order company offering short-term consumer credit to their consumers, we show that (i) given a certain sample size, sample bias has a significant effect on consumer credit-scoring performance and profitability, (ii) its effect is composed of the inclusion of rejected orders in the scoring model, and—to a lesser extent—the inclusion of these orders into the variable-selection process, and (iii) the impact of the effect of sample bias on consumer credit-scoring performance and profitability is modest.

Web mining based extraction of problem solution ideas

The internet is a valuable source of information where many ideas can be found dealing with different topics. A few numbers of ideas might be able to solve an existing problem. However, it is time-consuming to identify these ideas within the large amount of textual information in the internet. This paper introduces a new web mining approach that enables an automated identification of new technological ideas extracted from internet sources that are able to solve a given problem. It adapts and combines several existing approaches from literature: approaches that extract new technological ideas from a user given text, approaches that investigate the different idea characteristics in different technical domains, and multi-language web mining approaches. In contrast to previous work, the proposed approach enables the identification of problem solution ideas in the internet considering domain dependencies and language aspects. In a case study, new ideas are identified to solve existing technological problems as occurred in research and development (R&D) projects. This supports the process of research planning and technology development.

Enhanced decision support in credit scoring using Bayesian binary quantile regression

Fierce competition as well as the recent financial crisis in financial and banking industries made credit scoring gain importance. An accurate estimation of credit risk helps organizations to decide whether or not to grant credit to potential customers. Many classification methods have been suggested to handle this problem in the literature. This paper proposes a model for evaluating credit risk based on binary quantile regression, using Bayesian estimation. This paper points out the distinct advantages of the latter approach: that is (i) the method provides accurate predictions of which customers may default in the future, (ii) the approach provides detailed insight into the effects of the explanatory variables on the probability of default, and (iii) the methodology is ideally suited to build a segmentation scheme of the customers in terms of risk of default and the corresponding uncertainty about the prediction. An often studied dataset from a German bank is used to show the applicability of the method proposed. The results demonstrate that the methodology can be an important tool for credit companies that want to take the credit risk of their customer fully into account.

Identification of interdisciplinary ideas

Combine idea mining, semantic clustering, and classification.Identify ideas of an interdisciplinary nature.Improve decision making in innovation management. Literature shows interdisciplinary research as an essential driver for innovation. Ideas that are used as a starting point for this research are of an interdisciplinary nature because they combine aspects from different disciplines. The identification of interdisciplinary ideas at an early stage enables the start of interdisciplinary research and thus, it enables advances to be made in the innovation process. We propose a new methodology that combines semantic clustering and classification to estimate the interdisciplinary nature of ideas from a set of given ideas. The set is created automatically by use of an existing idea mining approach. Ideas from this set are semantically clustered to obtain concepts that are latent in the data. The relationship between each concept and each discipline pair from a set of given disciplines is calculated. Based on the degree of relationship, concepts are used to represent the interdisciplinary field spanned by the two disciplines. The ideas standing behind these concepts are identified as interdisciplinary ideas. As a result, the proposed methodology enables an estimation of the interdisciplinary nature of given ideas. The results might be helpful for researchers as well as for decision makers in the field of innovation management.

Wrapped Feature Selection For BinaryClassification Bayesian Regularisation NeuralNetworks: A Database Marketing Application

In this paper, we try to validate existing theory on and develop additional insight into repeat purchasing behaviour in a direct-marketing setting by means of an illuminating case study. The case involves the detection and qualification of the most relevant RFM (Recency, Frequency and Monetary) features, using a wrapped feature selection method in a neural network context. Results indicate that elimination of redundant/irrelevant features by means of the discussed feature selection method, allows to significantly reduce model complexity without degrading generalisation ability. It is precisely this issue that will allow to infer some very interesting marketing conclusions concerning the relative importance of the RFM-predictor categories. The empirical findings highlight the importance of a combined use of all three RFM variables in predicting repeat purchase behaviour. However, the study also reveals the dominant role of the frequency variable. Results indicate that a model including only frequency variables still yields satisfactory classification accuracy compared to the optimally reduced model.