Leveraging Blockchain and Federated Reinforcement Learning for Enhanced Fraud Detection in Financial Transactions - Tanweer Alam (SOTVI - Society of Visual Informatics, 2025)
Credit card fraud detection uses several technologies including machine learning (ML) and statistical analysis as well as card authentication methods. Credit card fraud is the illegal use of someone's credit card for purchase of goods or services. Comprehensive studies have shown how well ML technology generates exact prediction models to spot possible transaction fraud. Though the potential of losing the actual card is still a worry, hackers have been increasingly acquiring credit card numbers and personal information online. The growth in e-commerce has matched credit card use for online transactions, which has surged credit card theft. Complex detection systems including federated reinforcement learning (FRL) and blockchain technology have been developed to solve this challenge. Using standard pattern matching methods can make it difficult to tell real from fake transactions. A decentralised method of ML, FRL stresses user privacy and enhances anonymity and confidence in financial transactions. An original approach for teaching a credit card fraud detection model is shown in this work It makes advantage of user behaviour traits, federated reinforcement learning, and blockchain technologies. Using a smart contract between the bank and the client, the approach aims to reduce dishonest activity. By lowering the dependence on centralised data aggregation, this innovative approach guarantees user privacy protection all through model development. The research also looks at the challenges in credit card fraud detection and offers ideas for next developments.