Open AccessConvolutional Neural Network Approach for Mobile Banking Fraudulent Transaction to Detect Financial Frauds
Author(s) -
Soumya Shrivastava .,
Punit Kumar Johari
Publication year - 2022
Publication title -
international journal of engineering technology and management sciences
Language(s) - English
Resource type - Journals
ISSN - 2581-4621
DOI - 10.46647/ijetms.2022.v06i01.005
Subject(s) - credit card fraud , database transaction , computer science , financial transaction , identification (biology) , payment , convolutional neural network , digital currency , computer security , artificial intelligence , deep learning , financial instrument , money laundering , machine learning , credit card , finance , business , database , world wide web , botany , biology
In the real world, the identification of financial fraud in compliance with IoT criteria is highly effective since financial fraud causes financial damage. Several forms of financial fraud are likely, but unauthorized usage of mobile payment by credit card no. or certificate no.is the most common scenario. Detection of financial crime is a growing environment in which the victims will keep ahead. However, intelligent fraud detection facets remain scientifically unsupported. Deep learning (DL)arises from the idea of a multi-type representation of the human brain that incorporates basic characteristics at the low level or high-level abstractions.Financial fraud was a big issue as forgers discovered new methods of stealing currency. Therefore, adaptive methods of identification of fraud against forgers are required. Thanks to their versatile nature to detect emergent financial transaction fraud, deep learning approaches were enticing candidates. In this article, we suggest an in-depth learning approach for adapting financial fraud through the use of convolution neural networks (CNN). With the fraudulent transactions dataset, we tested our model experimentally. The results of the analysis show which our methods detect transactional fraud appropriately.