• A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection

      Viaene, Stijn; Derrig, Richard A.; Baesens, Bart; Dedene, Guido (+) (Journal of Risk and Insurance, 2002)
    • A multidimensional analysis of data quality for credit risk management: new insights and challenges

      Moges, H.; Dejaeger, Karel; Lemahieu, Wilfried; Baesens, Bart (Information and Management, 2013)
      Interest in group moods as an emergent phenomenon of group members’ interactions has significantly increased over the past two decades (Barsade & Gibson, 2007). Most studies focused particularly on understanding the effects of group moods on group processes (Barsade, 2001, Baartel & Saavedra, 2000, Barsade, Ward, Turner & Sonnenfled, 2000, Chiayu Tu, 2009) and group performance (Seung -Yoon Ree, 2006, Jordan, Lawrence & Troth, 2006). However, research investigating the antecedents of group moods is still scant. The current study fills this gap by focusing on the affective potential of group conflict. In this sense, group conflict focuses on how differences of opinion (task conflict) and person-related disagreements (relationship conflict) trigger group moods that differ in their valence (positive and negative) and level of activation (activated and unactivated) (Baartel & Saavedra, 2000). In this context, the group’s ability to define and understand its moods, their cause, evolution and relations between them - ability known as group emotional intelligence (Salovey & Mayer, 1990) - is expected to buffer the relation between conflict and group moods. By studying group moods in relation to group conflict, the current study extends previous research by considering group moods’ antecedents and not only their consequences. This contributes to a better understanding of group affect dynamics. In addition, the current study investigates different nuances of group moods given by different types of conflict. Whether an affect has a positive or negative valence, or whether it is activated or inactivated, has implications upon the further group dynamics.
    • Bank capital: a myth resolved

      Van Laere, Elisabeth; Baesens, Bart; Thibeault, André (Bank- en Financiewezen, 2008)
    • Bayesian neural networks for repeat purchase modelling in direct marketing

      Baesens, Bart; Viaene, Stijn; Van den Poel, Dirk; Vanthienen, Jan; Dedene, Guido (+) (European Journal of Operational Research, 2002)
    • Benchmarking least squares support vector machine classifiers.

      Van Gestel, Tony; Suykens, Johan A.K.; Baesens, Bart; Viaene, Stijn; Vanthienen, Jan; Dedene, Guido (+); De Moor, Bart; Vandewalle, J. (Machine learning, 2004)
    • Benchmarking state of the art classification algorithms for credit scoring

      Baesens, Bart; Van Gestel, Tony; Viaene, Stijn; Stepanova, Maria; Suykens, Johan A.K.; Vanthienen, Jan (Journal of the Operational Research Society, 2003)
    • Comprehensive rule-based compliance checking and risk management with process mining

      Caron, Filip; Vanthienen, Jan; Baesens, Bart (Decision Support Systems, 2013)
      Process mining researchers have primarily focused on developing and improving process discovery techniques, while attention for the applicability of process mining has been below par. As a result, there only exists a partial fit with the traditional requirements for compliance checking and risk management. This paper proposes a comprehensive rule-based process mining approach for a timely investigation of a complete set of enriched process event data. Additionally, the contribution elaborates a two-dimensional business rule taxonomy that serves as a source of business rules for the comprehensive rule-based compliance checking approach. Finally, the study provides a formal grounding for and an evaluation of the comprehensive rule-based compliance checking approach.
    • Determining the use of data quality metadata (DQM) for decision making purposes and its impact on decision outcomes — An exploratory study

      Moges, Helen-Tadesse; Van Vlasselaer, Véronique; Lemahieu, Wilfried; Baesens, Bart (Decision Support Systems, 2016)
      Decision making processes and their outcomes can be affected by a number of factors. Among them, the quality of the data is critical. Poor quality data cause poor decisions. Although this fact is widely known, data quality (DQ) is still a critical issue in organizations because of the huge data volumes available in their systems. Therefore, literature suggests that communicating the DQ level of a specific data set to decision makers in the form of DQ metadata (DQM) is essential. However, the presence of DQM may overload or demand cognitive resources beyond decision makers' capacities, which can adversely impact the decision outcomes. To address this issue, we have conducted an experiment to explore the impact of DQM on decision outcomes, to identify different groups of decision makers who benefit from DQM and to explore different factors which enhance or otherwise hinder the use of DQM. Findings of a statistical analysis suggest that the use of DQM can be enhanced by data quality training or education. Decision makers with a certain level of data quality awareness used DQM more to solve a decision task than those with no data quality awareness. Moreover, those with data quality awareness reached a higher decision accuracy. However, the efficiency of decision makers suffers when DQM is used. Our suggestion would be that DQM can have a positive impact on decision outcomes if it is associated with some characteristics of decision makers, such as a high data quality knowledge. However, the results do not confirm that DQM should be included in data warehouses as a general business practice, instead organizations should first investigate the use and impact of DQM in their setting before maintaining DQM in data warehouses.
    • Een overzicht van web usage mining en de implicaties voor e-commerce

      Souverein, M.; Baesens, Bart; Viaene, Stijn; Vanderbist, D.; Vanthienen, Jan (Beleidsinformatica Tijdschrift, 2001)
    • Financial Efficiency and Social Impact of Microfinance Institutions

      Louis, Philippe; Seret, Alex; Baesens, Bart (World development, 2013)
      This paper contributes to the literature by investigating whether the increased focus on financial self-sustainability of microfinance institutions has been disadvantageous to the target audience. We investigate the association between social efficiency and financial performance using a comprehensive data set that includes 650 microfinance institutions. A self-organizing map methodology is used to fully capture the existing heterogeneity among institutions. The results show that we cannot support the hypothesis that there exists a trade-off. On the contrary, we find evidence of a significant, positive relationship between social efficiency and financial performance.
    • Inferring comprehensible business/ICT alignment rules

      Cumps, Bjorn; Martens, David; De Backer, Manu; Haesen, Raf; Viaene, Stijn; Dedene, Guido; Baesens, Bart; Snoeck, Monique (Information and Management, 2009)
      We inferred business rules for business/ICT alignment by applying a novel rule induction algorithm on a data set containing rich alignment information polled from 641 organisations in 7 European countries. The alignment rule set was created using AntMiner+, a rule induction technique with a reputation of inducing accurate, comprehensible, and intuitive predictive models from data. Our data set consisted of 18 alignment practices distilled from an analysis of relevant publications and validated by a Delphi panel of experts. The goal of our study was to describe practical guidelines for managers in obtaining better alignment of ICT investments with business requirements. Our obtained rule set showed the multi-disciplinary nature of B/ICT alignment. We discuss implication of the alignment rules for practitioners.
    • Knowledge discovery in a direct marketing case using least squares support vector machines

      Viaene, Stijn; Baesens, Bart; Van Gestel, Tony; Suykens, Johan A.K.; Van den Poel, Dirk; Vanthienen, Jan; De Moor, Bart; Dedene, Guido (+) (International journal of Intelligent Systems, 2001)
    • Knowledge discovery in data: naar performante én begrijpelijke modellen van bedrijfsintelligentie

      Baesens, Bart; Mues, Christophe; Vanthienen, Jan (Business Inzicht, 2003)
    • The development of a simple and intuitive rating system under Solvency II

      Van Laere, Elisabeth; Baesens, Bart (Insurance: mathematics and economics, 2010)
    • Understanding and predicting bank rating transitions using optimal survival analysis models

      Louis, Philippe; Van Laere, Elisabeth; Baesens, Bart (Economics Letters, 2013)
      In the aftermath of the financial crisis, this study investigates which underlying determinants cause bank rating transitions. We develop survival analysis models to explain credit transition hazards using macroeconomic factors and the rating history. We find that there exists a significant dependence of rating upgrade or rating downgrade transition hazards on rating-specific covariates and macro-economic covariates. Our results confirm the momentum effect, meaning that a financial institution that has been recently upgraded/downgraded has a higher chance of being upgraded/downgraded again. The predictive performance of the developed models turns out to be satisfactory.
    • Wrapped input selection using multilayer perceptrons for repeat purchase modeling in direct marketing

      Viaene, Stijn; Baesens, Bart; Van den Poel, Dirk; Dedene, Guido (+); Vanthienen, Jan (International journal of Intelligent Systems in Accounting, 2001)