Deep Learning-Based Real-Time Credit Card Fraud Warning System

Abstract

There have been significant advancements in machine learning over the past couple decades, paving the way for the creation of the use and intelligent systems with enhanced data processing and categorization capabilities. Data validity (both logically and temporally) and timely feedback generation are essential to the systems' accuracy and precision. This study will focus on one such system a fraud detection system to better understand its inner workings. Investment in the study and creation of algorithmic and data analysis tools for use in detecting and preventing financial institution fraud is increasing. To solve this issue, numerous machine learning-based methods and algorithms have been presented. However, studies comparing Deep learning paradigms are scarce, and to the aimed to contribute, the works presented here fail to recognise the importance of a Real-time approach to problems of this kind. As a solution, we propose utilising deep neural network system to enhance a real-time fraud detection for credit cards approach. Our suggested model's auto-encoder base permits rapid verification or denial of credit card payments. We tested our model by contrasting it to four similar common forms of binary classifier models. Our proposed model outperforms the state-of-the-art solutions on the Benchmark dataset