As digital payments surge, the complexity of fraud continues to escalate, demanding more sophisticated tools to support risk analysts. This paper introduces the development of a Gen AI-assisted copilot integrated within a fraud detection solution, aimed at optimizing fraud alert investigation. The copilot leverages a Retrieval-Augmented Generation (RAG) model that combines a Random Forest (RF) machine learning model as the retriever and a fine-tuned Large Language Model (LLM) for generating contextual responses. By classifying fraud alerts, measuring similarity with historical data, and predicting the next action, the system automates decision-making while generating natural language recommendations for analysts. This approach significantly reduces manual workload, increases operational efficiency, and allows risk analysts to focus on critical, high-priority cases. The result is a robust system that enhances the speed and accuracy of fraud detection, investigation, and resolution, ultimately improving risk analyst productivity by almost 2.5 times.