Forgery Detection using Machine Learning (Credit Card Fraud)

Abstract

In e-commerce, user reviews can play a significant role in determining the revenue of an organization. Online users rely on reviews before making decisions about any product and service. As such, the credibility of online reviews is crucial for businesses and can directly affect companies’ reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyze and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions.