A data science project for detecting fraud in mobile money transactions using the PaySim dataset.
A fictional AML investigation case study demonstrating the identification and analysis of suspected structuring (smurfing) activities through transaction pattern analysis, customer due diligence, red flag identification, risk assessment, and compliance recommendations for educational and portfolio purposes.
Developed an AML Transaction Monitoring Dashboard using Power BI and Excel with a realistic banking dataset. The project includes customer risk profiling, PEP screening, transaction analysis, AML alerts, KPI dashboards, interactive slicers, and investigation reports to identify suspicious transactions and support compliance monitoring.
End-to-end machine learning pipeline for detecting credit card fraud using Azure Databricks, PySpark, MLlib, and MLflow automation.
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