The objective of this project is to build a Business Intelligence platform that analyzes financial transaction data to detect potential fraud and assess risk patterns. The system will leverage data analytics, visualization dashboards, and predictive modeling to enhance financial security and decision-making processes.
Collect financial transaction datasets (banking or simulated financial data).
Clean and preprocess transactional data for analysis.
Design a dimensional data warehouse for financial reporting.
Load data into a BI tool for visualization and reporting.
Create dashboards displaying transaction volume, anomaly trends, and risk indicators.
Implement anomaly detection algorithms (e.g., isolation forest or logistic regression).
Compare normal vs suspicious transaction patterns.
Develop visual alerts for unusual activities.
Validate predictive model performance using precision, recall, and F1-score.
Ensure data privacy and ethical handling of financial information.
Document findings and explain how BI supports fraud prevention strategies.
