Customer Churn prediction for FinTech Companies using Artificial Neural Networks

Pooja Malhotra*, Punit Patel**, Neel Shah***, Raj Veera****, Raj Sanghavi*****
*-** Department of Information Technology, K. J. Somaiya College of Engineering, Mumbai, India.
*** Ernst & Young Global Limited, Mumbai, Maharashtra, India.
**** Larsen & Toubro Infotech Limited, Mumbai, Maharashtra, India.
***** AllinCall Research and Solutions, Mumbai, Maharashtra, India.
Periodicity:December - February'2020
DOI : https://doi.org/10.26634/jcom.7.4.17373

Abstract

Over the years it has been observed that, market expansion in any sector has led to a huge customer base for service providers. This huge base comes with a variety of expectations. When these expectations are not met, it leads to dissatisfaction which ultimately leads to churn. Thus, in competitive markets, Customer Churn is a big problem for any company, causing huge loss of revenue. Our work contributes to developing a model that can predict potential customer for churn. We have used machine learning algorithm like SVM, Random Forest, Linear Regression to predict churn. We have compared different algorithms and speculated to use a method that could provide better accuracy. However, these algorithms do not provide the expected accuracy, so Artificial Neural Networks are applied in the dataset, which provides us with excellent accuracy.

Keywords

Customer Churn, Data Mining, Artificial Neural Networks, Linear Regression, Support Vector Machine, Random Forest.

How to Cite this Article?

Malhotra, P., Patel, P., Shah, N., Veera, R., & Sanghavi, R. (2020). Customer Churn Prediction for Fintech Companies using Artificial Neural Networks. i-manager's Journal on Computer Science, 7(4), 46-54. https://doi.org/10.26634/jcom.7.4.17373

References

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