Customer Attrition Estimation Modelling Based on Predominant Attributes Using Multi-layered Feed-Forward Neural Network

  • Vaishnavi SidhamshettiwarEmail author
  • Yash Gaba
  • Rutika Jadhav
  • Kiran Gawande
Conference paper
Part of the Algorithms for Intelligent Systems book series (AIS)


In an era of increasingly competitive businesses, the biggest challenge that firms face is retaining existing customers. When a customer leaves a business, the recurring revenue of an organization is highly impacted. In the banking and financial sector, churn rates are as high as 30%. Typically, products like home loans and education loans have the longest customer relationship. In this proposed work, an artificial neural network has been used to design a mathematical churn model which will assist financial organizations to reduce attrition and increase profits. An accuracy of 89.5% has been achieved.


Churn prediction Machine learning ANN 


  1. 1.
    Vafeiadis T, Diamantaras KI, Sarigiannidis G, Chatzisavvas KC (2015) A comparison of machine learning techniques for customer churn prediction. Department of Information Technology, TEI of Thessaloniki, GR-57400 Thessaloniki, GreeceCrossRefGoogle Scholar
  2. 2.
    Tsai CF, Lu YH (2009) Customer churn prediction by hybrid neural networks. Department of Information Management, National Central University, TaiwanCrossRefGoogle Scholar
  3. 3.
    Deloitte (2015) Opportunities in telecom sector: arising from big data. Aegis School of Business, Data Science and TelecommunicationGoogle Scholar
  4. 4.
  5. 5.
    Li C, Kang Q, Ge G, Song Q, Lu Q, Cheng J (2016) Deep BE: learning deep binary encoding for multi-label classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 39–46Google Scholar
  6. 6.
    Choong ACH, Lee NK (2017) Evaluation of convolutionary neural networks modeling of dna sequences using ordinal versus one hot encoding method. In: International conference on computer and drone applications, pp 60–65Google Scholar
  7. 7.
    Andrew G, Arora R, Bilmes J, Livescu K (2013) Deep canonical correlation analysis. In: International conference on machine learningGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Vaishnavi Sidhamshettiwar
    • 1
    Email author
  • Yash Gaba
    • 1
  • Rutika Jadhav
    • 1
  • Kiran Gawande
    • 1
  1. 1.Department of Computer EngineeringSardar Patel Institute of TechnologyMumbaiIndia

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