Abstract
This study proposes a behavior scoring model based on data envelopment analysis (DEA) to classify the customers into the high contribution and low contribution customers. Then, the low contribution customers are examined by using the slack analysis of DEA model to promote their contributions. The experiment results showed that the proposed method can provide indeed directions for bank to improve the contribution of the low contribution customers, and facilitates marketing strategy development.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Banasiak, M., O’Hare, E.: Behavior Scoring. Business Credit 103(3), 52–55 (2001)
Connors, M., Bona, S.: Scoring the Customer Lifecycle. Business Credit 105(2), 32–33 (2003)
Fritz, S., Hosemann, D.: Restructuring the Credit Process: Behaviour Scoring for German Corporates. Intelligent Systems in Accounting, Finance & Management 9(1), 9–21 (2000)
Thomas, L.C., Ho, J., Scherer, W.T.: Time Will Tell: Behavioural Scoring and the Dynamics of Consumer Credit Assessment. IMA Journal of Management Mathematics 12(1), 89–103 (2001)
Lin, Y.: Improvement on Behavior Scores by Dual-Model Scoring System. International Journal of Information Technology and Decision 1(1), 153–164 (2002)
He, J., Liu, X., Shi, Y., Xu, W., Yan, N.: Classifications of Credit Cardholder Behavior by Using Fuzzy Linear Programming. International Journal of Information Technology and Decision Making 3(4), 633–650 (2004)
Hsieh, N.C.: An Integrated Data Mining and Behavioral Scoring Model for Analyzing Bank Customers. Expert Systems with Applications 27(4), 623–633 (2004)
Hsieh, N.C.: Hybrid Mining Approach in the Design of Credit Scoring Models. Expert Systems with Applications 28(4), 655–665 (2005)
Frias-Martinez, E., Magoulas, G., Chen, S., Macredie, R.: Modeling Human Behavior in User-Adaptive Systems: Recent Advances Using Soft Computing Techniques. Expert Systems with Applications 29(2), 320–329 (2005)
Kou, G., Peng, Y., Shi, Y., Wise, M., Xu, W.: Discovering Credit Cardholders’ Behavior by Multiple Criteria Linear Programming. Annals of Operations Research 135(1), 261–274 (2005)
Larivičre, B., Van den Poel, D.: Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques. Expert Systems with Applications 29(2), 472–484 (2005)
Crook, J.N., Edelman, D.B., Thomas, L.C.: Recent Developments in Consumer Credit Risk Assessment. European Journal of Operational Research 183(3), 1447–1465 (2007)
Hadden, J., Tiwari, A., Roy, R., Ruta, D.: Computer Assisted Customer Churn Management: State-of-the-Art and Future Trends. Computers and Operations Research 34(10), 2902–2917 (2007)
Lim, M.K., Sohn, S.Y.: Cluster-Based Dynamic Scoring Model. Expert Systems with Applications 32(2), 427–431 (2007)
Chanes, A., Cooper, W.W., Rhodes, E.: Measuring the Efficiency of Decision Making Units. European Journal of Operational Research 2, 429–444 (1978)
Cooper, W.W., Seiford, L.M., Zhu, J.: Handbook on Data Envelopment Analysis. Kluwer Academic, Boston (2004)
Seiford, L.M., Thrall, R.M.: Recent Developments in DEA: The Mathematical Programming Approach to Frontier Analysis. Journal of Econometrics 46, 7–38 (1990)
Seiford, L.M.: Data Envelopment Analysis: The Evolution of the State of the Art (1978–1995). Journal of Productivity Analysis 7, 99–137 (1996)
Cherchye, L., Post, T.: Methodological Advances in DEA: A Survey and an Application for the Dutch Electricity Sector. Statistica Neerlandica 57(4), 410–438 (2003)
Emrouznejad, A., Parker, B.R., Tavares, G.: Evaluation of Research in Efficiency and Productivity: A Survey and Analysis of the First 30 Years of Scholarly Lliterature in DEA. Socio-Economic Planning Sciences 42(3), 151–157 (2007)
Cielen, A., Peters, L., Vanhoof, K.: Bankruptcy Prediction Using a Data Envelopment Analysis. European Journal of Operational Research 154(2), 526–532 (2004)
Chang, T.C., Chiu, Y.H.: Affecting Factors on Risk-Adjusted Efficiency in Taiwan’s Banking Industry. Contemporary Economic Policy 24(4), 634–648 (2006)
Al-Tamimi, H.A.H., Lootah, A.M.: Evaluating the Operational and Profitability Efficiency of a UAE-Based Commercial Bank. Journal of Financial Services Marketing 11(4), 333–348 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Chen, IF., Lu, CJ., Lee, TS., Lee, CT. (2009). Behavioral Scoring Model for Bank Customers Using Data Envelopment Analysis. In: Chien, BC., Hong, TP. (eds) Opportunities and Challenges for Next-Generation Applied Intelligence. Studies in Computational Intelligence, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92814-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-92814-0_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92813-3
Online ISBN: 978-3-540-92814-0
eBook Packages: EngineeringEngineering (R0)