Artificial Intelligence Powered Fraud Detection and Prevention Analysis of Application of Machine Learning in Online Transactions in Banking

The inability of conventional techniques to keep up with changing fraudulent strategies has made the integration of artificial intelligence (AI) and machine learning (ML) in online banking fraud detection crucial. Real-time detection capabilities provided by AI-powered models improve the capacity to spot irregularities and questionable patterns that may indicate fraudulent activity. However, there are drawbacks, especially with deep learning models, such as issues with data quality, scalability, and transparency. The purpose of this paper is to assess the efficacy of AI in preventing fraud, offer performance-enhancing solutions, and discuss important deployment issues along with proposing a solution for AI integration. The study emphasises how AI-driven fraud detection systems must have strong data management, real-time adaptability, and transparency.