Title: Customer retention and credit risk analysis using ANN, SVM and DNN

Authors: Nagaraj V. Dharwadkar; Priyanka S. Patil

Addresses: Department of Computer Science and Engineering, Rajarambapu Institute of Technology, Rajaramnagar, Islampur 415414, India ' Department of Computer Science and Engineering, Rajarambapu Institute of Technology, Rajaramnagar, Islampur 415414, India

Abstract: Nowadays, the banking sector are facing various challenges such as customer retention, fraud detection, risk management and customer segmentation. It can be possible to find solutions to these problems with the help of data analytics and machine learning (ML). In this paper, we have proposed a model which provides the solution to problems of the banking sector for customer retention and credit risk analysis. We used supervised learning techniques namely artificial neural network (ANN), support vector machine (SVM) and deep neural network (DNN) to analyse bank customer data. In order to analyse the algorithms we have used German credit dataset to evaluate the customer retention and credit risk. The experimental result shows that the using ANN, SVM and DNN algorithms, we could able to to reach recognition accuracy of 98%, 92% and 97% respectively for bank customer data and 72%, 72% and 76% for German credit dataset. The proposed method provides an efficient solution for retention and credit risk analysis of bank customers, which improves the profit of the banks by retaining the customers.

Keywords: banking industry; customer retention; credit risk analysis; machine learning; ML; classification; artificial neural network; ANN; support vector machine; SVM; deep neural network; DNN.

DOI: 10.1504/IJSSS.2018.095601

International Journal of Society Systems Science, 2018 Vol.10 No.4, pp.316 - 332

Received: 17 Aug 2017
Accepted: 21 Jul 2018
Published online: 11 Oct 2018 *

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