Research Article | Open Access
ONLINE PAYMENT FRAUD DETECTION
Mr. CH MAHESH BABU, BOJJA SWEEHONEY, PADAM PRATHYUSHA, BOMMEPALLI DEVENDRA REDDY, MAROJU SATHVIKA
Pages: 778-785
Abstract
As we are approaching modernity, the trend of paying online is increasing tremendously. The increase of use of online transactions is causing an increase in fraud. The frauds can be detected by using various approaches but they lag at accuracy and have some drawbacks. The online payment method leads to fraud that can happen using any payment app. The network transaction has the characteristics of low cost, wide coverage and high frequency, which makes the detection of fraud more complex. Aiming at the problem of difficult fraud detection in network transactions Fraud Detection is very important. Recent research has shown that machine learning techniques have been applied very effectively to the problem of payments related fraud detection. Such Machine Learning based technique have the potential to evolve and detect previously unseen patterns of fraud. So, we are going to use some machine learning algorithms to predict the transaction whether it is fraud or not. The selection of an algorithm to predict fraudulent transactions is based on attributes such as accuracy, recall, F1-score or etc. By considering these factors, we can identify the algorithm with the highest performance in terms of correctly identifying fraudulent transactions, ensuring the model’s effectiveness and reliability. The model can analyze the transaction data uploaded and return the fraud detection results to users. We show that our proposed approaches are able to detect fraud transactions with high accuracy and reasonably low number of false positives.
Keywords
Machine Learning, Online, Fraud, Detection, model, Transaction, Prediction, Algorithm.