International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.11, No.7, Jul. 2019
Enhanced Deep Feed Forward Neural Network Model for the Customer Attrition Analysis in Banking Sector
Full Text (PDF, 453KB), PP.10-19
In the present era with the development of the innovation and the globalization, attrition of customer is considered as the vital metric which decides the incomes and gainfulness of the association. It is relevant for all the business spaces regardless of the measure of the business notwithstanding including the new companies. As per the business organization, about 65% of income comes from the customer's client. The objective of the customer attrition analysis is to anticipate the client who is probably going to exit from the present business association. The attrition analysis also termed as churn analysis. The point of this paper is to assemble a precise prescient model using the Enhanced Deep Feed Forward Neural Network Model to predict the customer whittling down in the Banking Domain. The result obtained through the proposed model is compared with various classes of machine learning algorithms Logistic regression, Decision tree, Gaussian Naïve Bayes Algorithm, and Artificial Neural Network. The outcome demonstrates that the proposed Enhanced Deep Feed Forward Neural Network Model performs best in accuracy compared with the existing machine learning model in predicting the customer attrition rate with the Banking Sector.
Cite This Paper
Sandeepkumar hegde, Monica R Mundada, "Enhanced Deep Feed Forward Neural Network Model for the Customer Attrition Analysis in Banking Sector", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.7, pp.10-19, 2019. DOI: 10.5815/ijisa.2019.07.02
Hassani, Hossein, Xu Huang, and Emmanuel Silva. "Digitalisation and big data mining in banking.", Big Data and Cognitive Computing 2.3 (2018): 18.
Machowska, Dominika. "Investigating the role of customer churn in the optimal allocation of offensive and defensive advertising: the case of the competitive growing market", Economics and Business Review 4.2 (2018): 3-23.
Keramati, Abbas, Hajar Ghaneei, and Seyed Mohammad Mirmohammadi. "Developing a prediction model for customer churn from electronic banking services using data mining." ,Financial Innovation 2.1 (2016): 10.
Hatcher, William Grant, and Wei Yu. "A Survey of Deep Learning: Platforms, Applications, and Emerging Research Trends.", IEEE Access 6 (2018): 24411-24432.
Kanmani, W. S., and B. Jayapradha. "Prediction of Default Customer in Banking Sector using Artificial Neural Network.", International Journal on Recent and Innovation Trends in Computing and Communication 5.7 (2017): 293-296.
Mahajan, Deepika, and Rakesh Gangwar. "Improved Customer Churn Behaviour By Using SVM.", International Journal of Engineering and Technology, (2017):2395-0072.
Oyeniyi, A. O., et al. "Customer churn analysis in banking sector using data mining techniques.", Afr J Comput ICT 8.3 (2015): 165-174.
Bilal Zorić, Alisa. "Predicting customer churn in the banking industry using neural networks.", Interdisciplinary Description of Complex Systems: INDECS 14.2 (2016): 116-124.
Umayaparvathi, V., and K. Iyakutti. "Automated Feature Selection and Churn Prediction using Deep Learning Models.", International Research Journal of Engineering and Technology 4.3 (2017): 1846- 1846-1854.
P.K.D.M.Alwis, B.T.G.S.Kumara, Hapurachchi, ”Customer Churn Analysis and Prediction in Telecommunication for Decision Making”, International Conference on Buisness Innovation,25-26 August 2018,NSBM,Colombo,Srilanka.
Gregory, Bryan. "Predicting Customer Churn: Extreme Gradient Boosting with Temporal Data.", arXiv preprint arXiv:1802.03396 (2018)..
Dingli, Alexiei, Vincent Marmara, and Nicole Sant Fournier, "Comparison of Deep Learning Algorithms to Predict Customer Churn within a Local Retail Industry.", International Journal of Machine Learning and Computing, Vol. 7, No. 5, October 2017.
Vijaya, J., and E. Sivasankar. "An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing." ,Cluster Computing (2017): 1-12.
Ahmed, Ammar AQ, and D. Maheswari. "Churn prediction on huge telecom data using hybrid firefly based classification.", Egyptian Informatics Journal 18.3 (2017): 215-220.
Kaya, Erdem, et al. "Behavioral attributes and financial churn prediction." EPJ Data Science 7.1 (2018): 41.
Shirazi, Farid, and Mahbobeh Mohammadi. "A big data analytics model for customer churn prediction in the retiree segment." International Journal of Information Management (2018).
Jones, Pete R. "A note on detecting statistical outliers in psychophysical data." ,bioRxiv (2016): 074591.
Zhang, Qizhi, et al. "Large-scale classification in a deep neural network with Label Mapping.", arXiv preprint arXiv: 1806.02507 (2018).
The Thomas, et al. "Generalised Structural CNN's (SCNNs) for time series data with arbitrary graph-topologies.", arXiv preprint arXiv: 1803.05419 (2018).
Dingli, Alexiei, Vincent Marmara, and Nicole Sant Fournier. "Comparison of Deep Learning Algorithms to Predict Customer Churn within a Local Retail Industry.", International journal of machine learning and computing, (2017).
Ashia C., et al. "The marginal value of adaptive gradient methods in machine learning", Advances in Neural Information Processing Systems. 2017.