Table of Contents
Author: Pankaj Chowdhury, Komal Gupta, Madanu Akanksha
Abstract
In this current era, Mobile Banking adoption is revolutionizing financial management. This shift is fueled by the widespread adoption of mobile devices, creating numerous opportunities to redefine how we move and manage money. Mobile Banking Adoption, is determined by whether respondents reported using a mobile phone for financial transactions within the 12 months prior to the Nepal Demographic and Health Survey (DHS) 2022 survey or not. This research aims to study the potential factors associated with Mobile Banking Adoption by applying classical statistical analysis techniques and machine learning models. 14,845 women aged between 15–49 years in Nepal were included in the study. According to DHS Nepal 2022, only 9.24% of women reported using a mobile phone for financial transactions in the last 12 months. Notably, women with the highest education level (12-20 years) show 12.8 times higher odds of adoption compared to those with no education. Among the tree-based machine learning models we tested, the “Random Forest” emerged as one of the best models for predicting mobile banking adoption. After Analyzing the variable importance scores, it’s evident that education level, wealth index, and internet exposure are the strongest predictors of Mobile Banking adoption.
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Keywords: mobile banking in nepal, women empowerment in nepal, women’s economic empowerment in nepal, machine learning
References
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