An Effective Method to Understand Bank Customer Retention System
Tushar Suri1, Shailly Singh2, Tejkaran Singh3, Arul Kumar R4, S. Metilda Florence5

1Tushar Suri*, Department of IT, SRMIST, Chennai, India.
2Shailly Singh, Department of IT, SRMIST, Chennai, India.
3Tejkaran Singh, Department of IT, SRMIST, Chennai, India.
4Arul Kumar Rajappan, Department of IT, SRMIST, Chennai, India.
5Dr. S. Metilda Florence, Assistant Professor (Senior Grade), Department of IT, SRMIST, Chennai, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4279-4283 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8947038620/2020©BEIESP | DOI: 10.35940/ijrte.F8947.038620

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Abstract: Banking industry is one of those industries where data is generated every day in large amounts. This data can be used for extracting useful information. Hence it is important to store, process, manage and analyze this data. It helps in making business lucrative. This data helps in making prediction which helps in solving problems that are faced by banks these days. People are constantly working on various aspects of Banking System like fraud detection, Risk Analysis etc. Various Machine Learning algorithms like CNN, ANN etc. have been used in order to study the patterns from such datasets. Here, we are focusing on risk analysis, customer retention and customer segmentation. In this paper, we have implemented classification algorithm, namely Decision Tree, for different aspects. Training of model is done on the given data and testing is done on real time data provided by the user. This study might help various banking systems to gain knowledge about their investment scheme for a particular customer. Thus, the banking companies will have a greater control on their customer and can develop policies that will benefit both the parties.
Keywords: Banking Industry, Risk Analysis, Customer Retention, Customer Segmentation, Fraud Detection.
Scope of the Article: Predictive Analysis.