Fraud detection in digital payments using data analytics

• Main Authors: Para, Upendar, Srija, R. Krishna, Sowmikadurga, A.
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Artificial intelligence; Credit card fraud; Decision trees; Fraud prevention; Graphical representations; Machine learning
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0113356

Our project mainly focuses on detecting credit card fraud activities in real time scenarios. Nowadays this credit card fraud is increasing very rapidly. Some people use different techniques and trap innocent people and try to steal money from them. So, it's very crucial to get a proper method or a solution to control these types of activities. In our project we have created a model where it can detect fraud in each and every credit card transaction. This project can be used to detect various illegal transactions happening around. To get a proper solution for this we need to do something with the latest technologies we are having. Some of them are machine learning and artificial intelligence. Using these technologies, we can get a proper and accurate comeback solution. Coming to the solution it is like we will collect the data like credit card usage details which is set by the user and will keep it for experimental and trained dataset. It is done by using some of the algorithms like decision trees and random forest algorithms. Then we add the accuracy of the results data. Then we use some of the required attributes which can be used to detect the fraud in credit card transactions and it can be represented in a graphical model.