This paper presents a new approach for consumer credit scoring, by tailoring a profit-based classification performance measure to credit risk modeling. This performance measure takes into account the expected profits and losses of credit granting and thereby better aligns the model developers' objectives with those of the lending company. It is based on the Expected Maximum Profit (EMP) measure and is used to find a trade-off between the expected losses -driven by the exposure of the loan and the loss given default -and the operational income given by the loan. Additionally, one of the major advantages of * NOTICE: this is the author's version of a work that was accepted for publication in the European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Please cite this paper as follows: Verbraken, T., Bravo, C., Weber, R. and Baesens, B. (2014) Development and application of consumer credit scoring models using profit-based classification measures. European Journal of Operational Research. In Press. Available Online: http://www.sciencedirect.com/ science/article/pii/S0377221714003105 † Email Addresses: thomas.verbraken@kuleuven.be, crbravo@utalca.cl (corresponding), rweber@dii.uchile.cl, bart.baesens@kuleuven.be 1 using the proposed measure is that it permits to calculate the optimal cutoff value, which is necessary for model implementation. To test the proposed approach, we use a dataset of loans granted by a government institution, and benchmarked the accuracy and monetary gain of using EMP, accuracy, and the area under the ROC curve as measures for selecting model parameters, and for determining the respective cutoff values. The results show that our proposed profit-based classification measure outperforms the alternative approaches in terms of both accuracy and monetary value in the test set, and that it facilitates model deployment.
A c c e p t e d M a n u s c r i p t A novel profit-based feature selection method for churn prediction with SVM is presented. A backward elimination algorithm is performed to maximize the profit of a retention campaign. Our experiments on churn prediction datasets underline the potential of the proposed approaches. *Highlights (for review)Page 2 of 36 A c c e p t e d M a n u s c r i p Effective churners Outflow New customers InflowConsists of N customers with average customer lifetime value CLV.The cost of contacting a customer is f.The cost of an incentive offer is dWould-be churnersClassified as churners (η η η η)Would-be churnersClassified as nonchurners (1-η η η η)Customer Churn Management Campaign AbstractChurn prediction is an important application of classification models that identify those customers most likely to attrite based on their respective characteristics described by e.g. socio-demographic and behavioral variables.Since nowadays more and more of such features are captured and stored in the respective computational systems, an appropriate handling of the resulting information overload becomes a highly relevant issue when it comes to build customer retention systems based on churn prediction models. As a consequence, feature selection is an important step of the classifier construction process. Most feature selection techniques; however, are based on statistically inspired validation criteria, which not necessarily lead to models that optimize goals specified by the respective organization. In this paper we propose a profit-driven approach for classifier construction and simultaneous variable selection based on Support Vector Machines. Experimental results show that our models outperform conventional techniques for feature selection achieving superior performance with respect to business-related goals.
Customer churn prediction is becoming an increasingly important business analytics problem for telecom operators. In order to increase the efficiency of customer retention campaigns, churn prediction models need to be accurate as well as compact and interpretable. Although a myriad of techniques for churn prediction has been examined, there has been little attention for the use of Bayesian Network classifiers. This paper investigates the predictive power of a number of Bayesian Network algorithms, ranging from the Naive Bayes classifier to General Bayesian Network classifiers. Furthermore, a feature selection method based on the concept of the Markov Blanket, which is genuinely related to Bayesian Networks, is tested. The performance of the classifiers is evaluated with both the Area under the Receiver Operating Characteristic Curve and the recently introduced Maximum Profit criterion. The Maximum Profit criterion performs an intelligent optimization by targeting this fraction of the customer base which would maximize the profit generated by a retention campaign. The results of the experiments are rigorously tested and indicate that most of the analyzed techniques have a comparable performance. Some methods, however, are more preferred since they lead to compact networks, which enhances the interpretability and comprehensibility of the churn prediction models.
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