Overview
A collection of applied analyst scenarios demonstrating versatile, production-ready analytics skills across compliance and operations in a simulated banking environment. The work covers AML monitoring, forecasting, and data governance — prioritizing explainability and risk control.
Business Context
As an analyst supporting compliance and operations at a bank, different analytical methods were applied depending on the business risk: AML compliance monitoring, trend and seasonality forecasting, and data governance and ethics.
Analytical Approach
Methods Used
- Interpretable models (rules, thresholds, decision trees)
- Behavioral-based AML alert logic
- Time series trend and seasonality modeling
- PII vulnerability identification
Design Decisions
- Focused on practical, explainable techniques for regulated environments
- Standardized outputs into clear business decisions
- Demonstrated breadth across common banking use cases
Key Results
Business Impact
- Reduced discrimination and regulatory risk through bias-free AML alert logic
- Automated consistent decisions with forecast-driven planning
- Strengthened privacy and data governance controls
For a full technical breakdown including methodology, view this project on GitHub.
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