Prediction on Deposit Subscription of Customer based on Bank Telemarketing using Decision Tree with Entropy Comparison

Authors

  • Ardytha Luthfiarta Universitas Dian Nuswantoro
  • Junta Zeniarja Universitas Dian Nuswantoro
  • Edi Faisal Universitas Dian Nuswantoro
  • Wibowo Wicaksono Universitas Dian Nuswantoro

DOI:

https://doi.org/10.33633/jais.v4i2.2772

Abstract

Banking system collect enormous amounts of data every day. This data can be in the form of customer information,  transaction  details,  risk profiles,   credit   card   details,   limits   and   collateral    details, compliance  Anti Money Laundering (AML) related information, trade  finance  data,  SWIFT  and  telex  messages. In addition,  Thousands  of decision  are  made in Banking system. For example, banks everyday creates credit decisions,  relationship  start  up,  investment   decisions, AML  and  Illegal  financing  related decision.  To create this decision, comprehensive review on various  reports  and drills  down  tools  provided  by the banking systems is needed.  However, this is a manual process which  is  error  prone  and  time  consuming  due  to  large volume of transactional  and historical  data available. Hence, automatic knowledge mining is needed to ease the decision making process.  This research focuses on data mining techniques to handle the mentioned problem. The technique will focus on classification method using Decision Tree algorithms.  This research provides an overview of the data mining techniques and   procedures will be performed.   It also provides   an insight   into how these techniques can be used in deposit subscription  in banking system to make a decision making process easier and more productive. Keywords - Telemarketing, bank deposit, decision tree, classification, data mining, entropy.

References

Kazi Imran Moin, Dr. Qazi Baseer Ahmed, “Use of Data Mining in Bankingâ€, 2012.

Philip Kotler, Kevin Lane Keller, Framework for Marketing Management, 5th edition Pearson, 2012.

Fabrice Talla Nobibon, Roel Leus, Frits CR Spieksma, Optimization models for targeted offers in direct marketing: exact and heuristic algorithms, European Journal of Operational Research 210 (3) (2011) 670–683.

Efraim Turban, Ramesh Sharda, Dursun Delen, Decision Support and Business Intelligence Systems, 9th edition Pearson, 2011.

Pedro Domingos, A few useful things to know about machine learning, Communications of the ACM 55 (10) (2012) 78–87.

Paulo Cortez, Mark J. Embrechts, Using sensitivity analysis and visualization techniques to open black box data mining models, Information Sciences 225 (2013) 1–17.

David L. Olson, Dursun Delen, Yanyan Meng, Comparative analysis of data mining methods for bankruptcy prediction, Decision Support Systems 52 (2) (2012) 464–473.

Robert Phillips, Optimizing prices for consumer credit, Journal and Review Pricing Management 12 (2013) 360–377.

David Martens, Foster Provost, Pseudo-social network targeting from consumer transaction data, NYU Working Papers Series, , CeDER-11-05, 2011.

Sérgio Moro, Raul Laureano, Paulo Cortez, Enhancing bank direct marketing through data mining, Proceedings of the Forty- First International Conference of the European Marketing Academy, European Marketing Academy, 2012, pp. 1–8.

Vivek Bhambri “Application of Data Mining in Banking Sectorâ€, International Journal of Computer Science and Technology Vol. 2, Issue 2, June 2011

M Suman, T Anuradha, K Manasa Veena, “Direct Marketing with the Application of Data Miningâ€, 2011.

Smith Tsang, Ben Kao, Kevin Y. Yip, Wai-Shing Ho, and Sau

Dan Lee, “Decision Trees for Uncertain Dataâ€, 2011.

Pedro Domingos, A few useful things to know about machine learning, Communications of the ACM 55 (10) (2012) 78–87.

Dr. K. Chitra, B. Subashini, “Data Mining Techniques and Its

Application in Banking Sectorâ€, 2013.

Safia Abbas, “Deposit subscribe Prediction using Data Mining

Techniques based Real Marketing Datasetâ€, 2015.

Karl D. Majeske, Thomas W. Lauer, “The bank loan approval decision from multiple perspectivesâ€, 2013.

Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R., “CRISP-DM 1.0 - Step-by-step data mining guide, CRISP-DM Consortiumâ€, 2000

][Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data- Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, In press, http://dx.doi.org/10.1016/j.dss.2014.03.001

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Published

2020-03-06

Issue

Vol. 4 No. 2 (2019): Journal of Applied Intelligent System

Section

Articles

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