Citation:

Devendra Singh Parmar, Harshad Pitkar, Hemlatha Kaur Saran, Pankaj Gupta, 2024. "AI in Designing New Payment Processing Systems for Fraud Detection", ESP Journal of Engineering & Technology Advancements  4(4): 127-132.

Abstract:

Complex digital payment systems make them more prone to fraud, raising the need for advanced fraud detection solutions. Since rule-based systems cannot keep up with fraudsters' ever-changing schemes, AI is needed to prevent fraud. This research examines how AI could be used to detect fraud in future payment processing systems to improve efficiency, accuracy, and security. AI models can evaluate enormous information in real time using deep learning, decision trees, and neural networks to detect fraudulent activities that people neglect. The study uses mixed methods to combine quantitative model performance indicators (F1 score, recall, accuracy, and precision) with qualitative financial case study findings. Deep learning models use more system resources, but our research demonstrates that they identify fraud more accurately and recall than decision trees. Results show that AI models dramatically reduce false positives, which benefits customers and businesses. AI in payment systems reduces fraud losses and speeds up transaction processing, providing financial benefits. However, ethical challenges including algorithmic bias and lack of transparency in AI decision-making still prevent AI acceptance. These challenges must be overcome for financial services companies to deploy AI-driven fraud detection systems. The report shows how AI can revolutionise payment system fraud detection and discusses AI implementation pros and cons. As efficiency and security remain priority, AI will determine how financial institutions detect fraud in the future.

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Keywords:

: Artificial Intelligence, Fraud Detection, Payment Systems, Machine Learning, Deep Learning, Financial Security, Real-time Processing, Algorithmic Bias, Financial Technology.