Case Study: Financial Compliance
Designing and implementing an anomaly detection system on Snowflake.
The Problem
OUC (the organization) manages thousands of transactions monthly. Ensuring compliance with strict financial regulations was a manual, time-intensive process prone to human error and oversight.
The core challenge was to transition from reactive manual checks to a proactive, automated monitoring system that could scale with transaction volume.
The Data
The pipeline began with raw transaction data across various formats. I built ingestion systems that consolidated this data into a centralized Snowflake environment.
Key data points included:
- Transaction amounts and timestamps
- Merchant Category Codes (MCC)
- Geographical transaction markers
- User behavioral profiles
Extensive cleaning and normalization were performed to ensure metadata consistency across disparate sources.
Technical Approach
I designed a multi-stage ETL pipeline using Python and Snowflake’s internal processing power.
The system leverages anomaly detection models to flag outliers in real-time. Instead of simple rule-based filters, we utilized statistical modeling to identify complex patterns that might indicate non-compliance or fraudulent activity.
Evaluation & Performance
Success was measured by the system's ability to minimize false positives while capturing legitimate compliance risks.
Deployment & Infrastructure
The infrastructure is built entirely on the Snowflake platform, utilizing native capabilities for storage, compute, and modeling.
While this project didn't require a lakehouse architecture like Delta Lake, the pipeline design was influenced by modern lakehouse principles to ensure future scalability and data interoperability.
Impact
The primary impact was the shift toward automated oversight. The system significantly reduced the manual workload for the compliance team, allowing them to focus on high-risk investigations rather than routine scanning.
The project is currently evolving into a fully integrated part of the financial governance framework.
Collaboration & Safety
Engineering doesn't happen in a vacuum. I collaborated closely with:
Support Provided
- Problem scoping & definition
- Pipeline design & iteration
- Documentation & knowledge sharing
Stakeholder Interaction
- Compliance requirement alignment
- Infrastructure design with platform teams
- Model review with SMEs