Title: Logistic regression vs. artificial neural network model in prediction of financial inclusion: empirical evidence from PMJDY program in India

Authors: R. Magesh Kumar; G. Delina; R. Senthil Kumar; S. Siamala Devi

Addresses: School of Management, Presidency University, Bangalore, Karnataka, India ' Department of Business Administration, Kalasalingam Business School (KBS), Kalasalingam Academy of Research and Education (Deemed to be University), Rajapalayam, Tamil Nadu, India ' School of Management, Presidency University, Bangalore, Karnataka, India ' Department of Information Technology, Srikrishna college of Technology, Coimbatore, Tamil Nadu, India

Abstract: Pradhan Mantri Jandhan Yojana (PMJDY) is a financial inclusion program launched by the Government of India in 2014 to deliver various banking services through a basic bank account feature to the vulnerable population. The primary objective of this study is to find if there is a significant difference between the two predictive models - Logistic Regression (LR) and Artificial Neural Network (ANN) in terms of classification accuracy on forecasting the account usage among the two groups of customers i.e. regular users and non-regular users. The study also uncovers the significant predictors that are important in forecasting the account usage. The results suggest both the LR and ANN models have shown good prediction accuracy. However, the findings indicate the Multilayer Perceptron Neural Network (MLPNN) using the standardised rescaling approach of a covariate has a slight better prediction than the LR model with a correct classification rate of 82.8% in the testing and validating stage of the sample cases. The practical implications of the study will provide meaningful results to the banking authorities, bureaucrats and policymakers for enriching the financial services to the underprivileged segment of the population.

Keywords: financial inclusion; financial services; financial literacy; PMJY; Pradhan Mantri Jandhan Yojana; artificial neural network; logistic regression.

DOI: 10.1504/IJFSM.2022.126865

International Journal of Financial Services Management, 2022 Vol.11 No.3, pp.245 - 267

Accepted: 23 Mar 2022
Published online: 09 Nov 2022 *

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