For this challenge, I analyzed banking transaction data to uncover trends in transaction performance, fraud exposure, customer behavior, and revenue health. Using SQL for data exploration, validation, and anomaly detection, I identified key patterns and business issues within the dataset. The data was then transformed and modeled in Power BI, where DAX measures and interactive dashboards were developed to support business decision-making. The analysis revealed that fraud exposure was concentrated among just four customers, with a single customer contributing the majority of the risk. I also identified significant revenue leakage caused by systematic undercharging of international transfers, despite overall fee revenue appearing accurate. Additional findings highlighted operational inefficiencies in transaction completion rates, elevated fraud risk within Fuel and Gambling sectors, and the limited effectiveness of KYC status as a fraud indicator.

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