UPI FRAUD DETECTION SYSTEM

Abstract

Now a days Digital transactions are rapidly
increasing as it results in increasing online payment
frauds too. In fact, according to the Reserve Bank of
India, comparing March 2022 to March 2019,
digital payments have risen in volume and value by
216% and 10%, respectively. People are starting to
go all-in with digital transactions, but one can’t
deny the security issues that loom, and know-how
when it comes to online payments. Few years ago,
we could have barely seen the online payment, but
today UPI payment QR code installed at doorstep.
This invited the hoaxers and attackers to develop
fraudulent transactions and fool people for some
amount of money. Fortunately, the online
transactions are monitored and hence could be
analysed using the latest tools. In this system, an
attempt is made to develop a machine learning
model to identify fraudulent transactions in a
transaction’s dataset. This study considers the task
of applying artificial intelligence to recognize bank
fraud. In recent years, due to the COVID-19
pandemic, bank fraud has become even more
common due to the massive transition of many
operations to online platforms and the creation of
many charitable funds that criminals can use to
deceive users. The study’s scientific novelty is the
development of machine learning models for
identifying fraudulent banking transactions and
techniques for preprocessing bank data for further
comparison and selection of the best results. This
paper also details various methods for improving
detection accuracy, i.e., handling highly imbalanced
datasets, feature transformation, and feature
engineering. The recognition of banking fraud using
artificial intelligence algorithms is a topical issue in
our digital society.