A multidimensional analysis of data quality for credit risk management: New insights and challenges

Moges, Helen-Tadesse; Dejaeger, Karel; Lemahieu, Wilfried; Baesens, Bart

doi:10.1016/j.im.2012.10.001

Cited by 41 publications

(27 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, our work cannot be directly related to the CEP domain, as we approach the concept of an ''event'' from a different semantic perspective, namely its meaning and its types within a business processes analytics and process mining environment, that is, events as they occur in information system logs. More closely related to the presented research are thus the contributions in the context of data quality, with dimensions such as data accuracy, data completeness and data security [16].…”

Section: Problem Identification and Motivationmentioning

confidence: 95%

On the gap between reality and registration: a business event analysis classification framework

et al. 2016

Self Cite

View full text Add to dashboard Cite

This paper presents a business event analysis classification framework, based on five business criteria. As a result, we are able to distinguish thirteen event types distributed over four categories, i.e. truthful, invisible, false and unobserved events. Currently, several of these event types are not commonly dealt with in business process management (BPM) and analytics (BPA) research. Based on the proposed framework we situate the different BPM and BPA research areas and indicate the potential issues for each field. A business case is elaborated to demonstrate the relevance of the event classification framework.

show abstract

Section: Problem Identification and Motivationmentioning

confidence: 95%

On the gap between reality and registration: a business event analysis classification framework

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Database composition is of particular interest and relevance in FDSSs because of the introduction of compliance guidelines, such as Basel II and Basel III (Moges, Dejaeger, Lemahieu, & Baesens, 2013). As the latter has a straight impact on the capital buffers and, hence, on the protection of financial organizations, individual regularity attention is being given to addressing dataset gathering from a different domain for authenticating the prediction classifier.…”

Section: Real-world Fdsss Datasetmentioning

confidence: 99%

Topological applications of multilayer perceptrons and support vector machines in financial decision support systems

et al. 2018

View full text Add to dashboard Cite

The heart of this study is particularly on risk assessment of financial decision support systems (FDSSs), to advance the model performance and improve classification accuracy. To conquer the downsides of the classical models, statistical intelligence (SI) technologies, for example, multilayer perceptrons (MLPs) and support vector machines (SVMs), have been deliberated in FDSS applications. Recently, the prestigiousness of SI approaches has been confronted by the latest prediction learners. Therefore, to ensure the competitive performance of SI mechanisms, the current investigation scrutinizes the topological applications of MLPs and SVMs over eight different databases with equivalent combinations in credit scoring and bankruptcy predictions example sets. The experimental results reveal that MLP5‐5 and MLP4‐4, that is, the sigmoid activation function with five and four hidden layers, are the feasible topologies for the MLP algorithm, and on all databases in all performance criterions, SVM trained with the linear kernel function (SVM‐1) achieves better prediction results. From the “Baseline” family, random forest learner brings significant improvements in financial decisions. Lastly, FDSSs are found to be correlated with the nature of databases and the performance criterions of the trained algorithms. The results of this study, however, have practical and managerial implications to make a range of financial and nonfinancial strategies. With these contributions, therefore, our study not only supplements earlier evidence but also enhances the predictive performance of SI algorithms for financial decision support applications.

show abstract

“…The work in [47], like many works found in the literature, is based on the DQ dimensions classification given in [3], where DQ dimensions are classified into: intrinsic (based on the degree to which data values adjust to real values), contextual (based on the degree to which data are applicable to user's task), representational (based on the degree to which data are presented in an intelligible and clear way) and accessibility (based on the degree to which data are available). For example, accuracy and objectivity are objective dimensions since they are intrinsic to the data and independent from the context where they are used.…”

Section: How Contexts Are Defined and Used In Dws?mentioning

confidence: 99%

“…However, not all DQ dimensions can be objectively measured, since dimensions such as relevance and believability tend to vary according to the context where they are used. In [47] the authors claim that due to the dependence of DQ on the context, despite the wide discussions about DQ dimensions existing in the literature, it does not exist a unified set of DQ dimensions.…”

Section: How Contexts Are Defined and Used In Dws?mentioning

confidence: 99%

Context-based Data Quality Metrics in Data Warehouse Systems

Serra¹,

Marotta²

2017

CLEIej

View full text Add to dashboard Cite

The fact that Data Quality (DQ) depends on the context, in which data are produced, stored and used, is widely recognized in the research community. Data Warehouse Systems (DWS), whose main goal is to give support to decision making based on data, have had a huge growth in the last years, in research and industry. DQ in this kind of systems becomes essential. This work presents a proposal for identifying DQ problems in the domain of DWS, considering the different contexts that exist in each system component. This proposal may act as a first conceptual framework that guides the DQ-responsible in the management of DQ in DWS. The main contributions of this work are a thorough literature review about how contexts are used for evaluating DQ in DWS, and a proposal for assessing DQ in DWS through contextbased DQ metrics.

show abstract

A multidimensional analysis of data quality for credit risk management: New insights and challenges

Cited by 41 publications

References 29 publications

On the gap between reality and registration: a business event analysis classification framework

On the gap between reality and registration: a business event analysis classification framework

Topological applications of multilayer perceptrons and support vector machines in financial decision support systems

Context-based Data Quality Metrics in Data Warehouse Systems

Contact Info

Product

Resources

About