Snehankita Majalekar
As the growth of the FinTech platforms continues, there is an increasing demand for intelligent, secure and traceable solutions that can provide real-time detection of fraudulent transactions and shield financial records from manipulation. In this research, an Artificial Intelligence-powered blockchain framework, combining machine learning for fraud detection and permissioned blockchain for validation, was proposed. It was found that ensemble models performed better than a linear baseline. The overall best balance of precision, specificity and F1 score was obtained with the Random Forest model, and the highest precision–recall was obtained with the Extra Trees model, with fraud recall slightly better. In addition, feature-importance analysis revealed a small number of transaction attributes, which were anonymised, that most significantly affected fraud classification. The chosen model was then connected to a prototype of a chained hash blockchain that preserved the hashes of transactions, the time, the predicted probability of fraud, the validation result, and the version of the model. Through hash inconsistency, the prototype was able to detect any transaction modifications which might have been made on purpose and successfully ensured ledger integrity. The results illustrate how both AI and blockchain technologies complement each other. AI is effective in detecting fraud accurately and on time, and blockchain enhances the traceability, auditability and tamper resistance of transactions.