526 research outputs found
Optimizing the collections process in consumer credit
No full textThis paper builds a dynamic programming model to optimize the collections process in consumer credit. It determines which collections actions should be undertaken and how long they should be performed. Theoretical results about the form of the optimal policy under certain conditions are obtained. Finally a case study is described based on data from the collection department of a European ban
Building intelligent credit-risk evaluation systems using neural network rule extraction and decision tables
No full textTable of contentsNeo-classical reengineering: Returning to the promise of process in the post-Internet economyM. De Kegel and M. McDonaldTowards an integrative framework for software architectureR. Maes and G. DedeneComponent based development. From dinosaurs to small, adaptive, co-operating, replaceable creaturesG. Van Humbeeck, J. MerckxSeparating Business Process Aspects from Business Object behaviourM. SnoeckCOSMIC-FFP and MERODE: Applying the Next Generation Function Points to Object Oriented Enterprise ModelsG. PoelsOn the use of Jackson Structured Programming (JSP) for the structured design of XSL TransformationsG. DedeneRuling the business: about Business Rules, decision tables and Intelligent AgentsJ. VanthienenBuilding intelligent credit-risk evaluation systems using neural network rule extraction and decision tablesB. Baesens, R. Setiono, C. Mues, S. Viaene and J. VanthienenWeb service description, advertising and discovery: WSDL and beyondW. LemahieuDeveloping enterprise architecture: the case of KBC InsuranceF. Pieck, S. Viaene and G. Deden
An experimental comparison of classification algorithms for imbalanced credit scoring data sets
No full textPredicting loss given default (LGD) for residential mortgage loans: a two-stage model and empirical evidence for UK bank data
No full textWith the implementation of the Basel II regulatory framework, it became increasingly important for financial institutions to develop accurate loss models. This work investigates the loss given default (LGD) of mortgage loans using a large set of recovery data of residential mortgage defaults from a major UK bank. A Probability of Repossession Model and a Haircut Model are developed and then combined to give an expected loss percentage. We find that the Probability of Repossession Model should consist of more than just the commonly used loan-to-value ratio, and that the estimation of LGD benefits from the Haircut Model, which predicts the discount which the sale price of a repossessed property may undergo. This two-stage LGD model is shown to perform better than a single-stage LGD model (which models LGD directly from loan and collateral characteristics), as it achieves a better R2 value and matches the distribution of the observed LGD more accurately
An experimental comparison of classification algorithms for imbalanced credit scoring data sets
No full textIn this paper, we set out to compare several techniques that can be used in the analysis of imbalanced credit scoring data sets. In a credit scoring context, imbalanced data sets frequently occur as the number of defaulting loans in a portfolio is usually much lower than the number of observations that do not default. As well as using traditional classification techniques such as logistic regression, neural networks and decision trees, this paper will also explore the suitability of gradient boosting, least square support vector machines and random forests for loan default prediction.Five real-world credit scoring data sets are used to build classifiers and test their performance. In our experiments, we progressively increase class imbalance in each of these data sets by randomly under-sampling the minority class of defaulters, so as to identify to what extent the predictive power of the respective techniques is adversely affected. The performance criterion chosen to measure this effect is the area under the receiver operating characteristic curve (AUC); Friedman’s statistic and Nemenyi post hoc tests are used to test for significance of AUC differences between techniques.The results from this empirical study indicate that the random forest and gradient boosting classifiers perform very well in a credit scoring context and are able to cope comparatively well with pronounced class imbalances in these data sets. We also found that, when faced with a large class imbalance, the C4.5 decision tree algorithm, quadratic discriminant analysis and k-nearest neighbours perform significantly worse than the best performing classifiers.<br/
Predicting loss given default for residential mortgage loans: a two stage model and empirical evidence for UK bank data
No full textModelling LGD for unsecured personal loans: decision tree approach
No full textThe New Basel Accord, which was implemented in 2007, has made a significant difference to the use of modelling within financial organisations. In particular it has highlighted the importance of Loss Given Default (LGD) modelling. We propose a decision tree approach to modelling LGD for unsecured consumer loans where the uncertainty in some of the nodes is modelled using a mixture model, where the parameters are obtained using regression. A case study based on default data from the in-house collections department of a UK financial organisation is used to show how such regression can be undertaken
Domain knowledge integration in data mining using decision tables: case studies in churn prediction
No full textCompanies' 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 contribution of this paper is to show how domain knowledge can be incorporated in the data mining process for churn prediction, viz. through the evaluation of coefficient signs in a logistic regression model, and secondly, by analysing a decision table (DT) extracted from a decision tree or rule-based classifier. An algorithm to check DTs for violations of monotonicity constraints is presented, which involves the repeated application of condition reordering and table contraction to detect counter-intuitive patterns. Both approaches are applied to two telecom data sets to empirically demonstrate how domain knowledge can be used to ensure the interpretability of the resulting models
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