Predicting Satisfaction of Online Banking System in Bangladesh by Machine Learning

Abstract

Online banking refers to using your smartphone, tablet, or another internet-connected computer to browse and access your bank account. It is quick and free, and it usually allows you to perform a variety of activities, such as paying bills and exchanging currency, without having to visit or call your branch. As a developing nation, Bangladesh is seeing an increase in online banking. People are still reliant on online banking because it makes a man's life much easier. During the Corona incident, the use of online banking increased at an unprecedented pace. Online banking services such as Rocket, bKash, and Nagad are now available in the region. While online banking makes life easier, third-party money laundering incidents do occur from time to time. As a result, some people are unhappy with online banking. However, some people say that they are happy with their online banking experience. This work tries to address this critique and give the right advice to the customer. Customer satisfaction and frustration with online banking have been predicted using Machine Learning techniques in this study. Seven traditional machine learning classification algorithms Logistic Regression, Random Forest, Naïve Bayes, support vector machine, Neural network, Decision tree, K nearest neighbor algorithms to complete this research work and find the concluded delimiter.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 26 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.