This project analyzes 2,133 payment transactions to identify fraud patterns and high-risk segments using Power BI. The dashboard transforms raw transactions data into actionable business insights through structured data cleaning, modeling, and visualization. Before building the dashboard, the dataset was explored and pre-processed in Microsoft Excel, where initial data validation, duplicate checks, formatting standardization, and exploratory analysis were performed. The cleaned dataset was then imported into Power BI for advanced transformation using Power Query and KPI development using DAX. The objective is to detect fraud concentration across payment methods, locations, devices, and time periods to support data-driven risk management decisions.