Abstract
Online transactions are becoming more popular in present situation where the globe is facing an unknown disease COVID-19. Now authorities of several countries have requested people to use cashless transaction as far as possible. Practically, it is not always possible to use it in all transactions. Since number of such cashless transactions has been increasing during lockdown period due to COVID-19, fraudulent transactions are also increasing in a rapid way. Fraud can be analysed by viewing a series of customer transactions data that was done in his/ her previous transactions. Normally banks or other transaction authorities warn their customers about the transaction, if they notice any deviation from available patterns; the authorities consider it as a possibly fraudulent transaction. For detection of fraud during COVID-19, banks and credit card companies are applying various methods such as data mining, decision tree, rule based mining, neural network, fuzzy clustering approach and machine learning methods. The approach tries to find out normal usage pattern of customers based on their former activities. The objective of this paper is to propose a method to detect such fraud transactions during such unmanageable situation of the pandemic. Digital payment schemes are often threatened by fraudulent activities. Detecting fraud transactions during money transfer may save customers from financial loss. Mobile-based money transactions are focused in this paper for fraud detection. A Deep Learning (DL) framework is suggested in the paper that monitors and detects fraudulent activities. Implementing and applying Recurrent Neural Network on PaySim generated synthetic financial dataset, deceptive transactions are identified. The proposed method is capable to detect deceptive transactions with an accuracy of 99.87%, F1-Score of 0.99 and MSE of 0.01.
Publisher
Advanced Research Publications
Cited by
12 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Using Complex Network Analysis Techniques to Uncover Fraudulent Activity in Connected Healthcare Systems;Advances in Systems Analysis, Software Engineering, and High Performance Computing;2024-06-14
2. Design and Implementation of a Web-Based Credit Card Fraud Detection System;2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG);2024-04-02
3. Secure UPI: Machine Learning-Driven Fraud Detection System for UPI Transactions;2024 2nd International Conference on Disruptive Technologies (ICDT);2024-03-15
4. Spoofing Transaction Detection with Group Perceptual Enhanced Graph Neural Network;Lecture Notes in Computer Science;2024
5. Methods of Machine Learning in Epidemic Risk Assessment: The Case of the Covid-19;2023 4th International Conference for Emerging Technology (INCET);2023-05-26