Abstract - Fraud is a major problem in many industries, with pretenses being used to obtain money or property. Fraud detection is a crucial process that involves combining various datasets to create a comprehensive view of payment data to make informed decisions. Credit card fraud detection with deep learning is a method of data investigation by a data science team that uses all meaningful features of card users' transactions to identify fraudulent activities. The data is then processed by a trained model that finds patterns and rules to classify transactions as legitimate or fraudulent. Once integrated into an e-commerce platform, the deep learning-driven fraud protection module tracks transactions and determines the probability of fraud. Based on the predicted probability, transactions may be allowed, require additional authentication, or be frozen for manual processing. Fraud is a persistent problem in the financial industry, costing companies billions of dollars each year. Fraudulent transactions can take many forms, from stolen credit card information to account takeover attacks. As such, companies need to employ robust fraud detection methods to protect themselves and their customers. One popular method for detecting fraud is through the use of deep learning. Deep learning is a type of artificial intelligence that uses neural networks to analyze large amounts of data and identify patterns. By training a deep learning model on historical transaction data, a company can create a system that is capable of accurately detecting fraudulent transactions in real time. To develop a deep learning-based fraud detection system, a data science team will first gather and pre-process data from a variety of sources, including transaction logs, user behavior patterns, and other relevant data points. They will then use this data to train a deep learning model, which will learn to identify patterns and relationships between different data points that are indicative of fraudulent activity. Once the model is trained, it can be deployed within the company's payment processing system.
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DOI:
10.17148/IJARCCE.2023.12545
