Enhancing Fraud Detection in Payment Systems with Generative AI and IoT Integration

Fraud detection in payment systems poses significant challenges to secure transactions in a digital economy.The rapid expansion of digital payment systems has amplified the challenges of detecting fraudulent activities, with conventional methods struggling to adapt to evolving fraud patterns and high transaction volumes.This study introduces an advanced framework for enhancing fraud detection in payment systems by integrating Generative AI and IoT technologies.The proposed system employs IoT-enabled payment devices to capture real-time transactional data, which is preprocessed and analyzed using Generative Adversarial Networks (GANs) to generate synthetic fraudulent patterns for model training.Additionally, discriminative AI models classify transactions based on enriched datasets, leveraging deep learning techniques for anomaly detection.This approach addresses key challenges such as data imbalance, dynamic fraud behaviors, and realtime detection requirements.Experimental validation on benchmark datasets and IoT-enabled payment devices demonstrates significant improvements in detection accuracy, reduced false positives, and scalability.The integration of IoT and AI technologies not only enhances fraud detection capabilities but also provides a secure, adaptive, and efficient solution for modern payment systems.This research highlights the transformative potential of combining Generative AI with IoT in combating financial fraud effectively.The integration of IoT (Internet of Things) and Generative AI technologies offers a promising solution to proactively detect and mitigate fraudulent activities.This paper proposes a system architecture for fraud detection leveraging IoT devices with generative AI to monitor transactions in real-time and to predict and identify fraudulent patterns.By combining data collected from IoT-enabled smart card systems with advanced AI algorithms, the proposed solution enhances accuracy and reduces false positives in fraud detection.This work provides a comprehensive framework, including real-time monitoring, anomaly detection, and adaptive learning mechanisms, to ensure a robust and scalable fraud detection system.These activities are administered so elegantly so it's almost like genuine transactions, the Sorting and Searching technique was used for oversampling.As we are integrating the IoT for double verification for the transaction to scale back the fraudulent transaction the NFC technology plays an important role in the re-authentication of the user feature selection was performed and the dataset was split into two parts, training data, and test data.The algorithms utilized in the experiment were Logistic Regression and high accuracy using Generative AI and Prompt integration techniques.