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Case study
Golden 1 Credit Union: Fraud-Monitoring Log Pipeline
Software Engineer, Trivir. Client: Golden 1 Credit Union. Built and deployed the Ping AIC to Verafin pipeline.
- High-volume event stream
- Fraud/AML-ready records
- Resilient in production
Golden 1 Credit Union: Fraud-Monitoring Log Pipeline
Impact
- Built, tested, and deployed a production Ruby/Fluentd pipeline that transformed a high volume of Ping Advanced Identity Cloud login and logout events into structured Verafin fraud-monitoring records.
- Delivered a single, normalized record shape that fraud and AML workflows could consume reliably.
- Hardened the pipeline against real production failure modes so ingestion stayed dependable.
Problem Raw Ping authentication logs did not match Verafin's schema. The first transform failed on malformed fields, missing values, and delimiter collisions.
Constraints
- Banking compliance: accurate, auditable transforms for fraud/AML.
- Reliability: the stream runs continuously; failures can't drop events.
- Data quality: inputs are inconsistent and must be normalized safely.
- Operations: low-touch, with diagnostics when something goes wrong.
Role Software Engineer, IAM. Built, tested, and deployed the pipeline in Ruby and Fluentd.
Approach
- Implemented event mapping and field normalization in Fluentd using Ruby filters.
- Added resilient field fallbacks, status normalization, and delimiter-safe serialization to survive malformed input.
- Diagnosed and fixed control-flow, nil-response, and runtime failures surfaced in production.
- Added local diagnostic logging and validated output against the Verafin specification.
Results
- Auditable, compliant ingestion with a single normalized record shape.
- Eliminated manual reconciliation by enforcing consistent, well-formed records.
- Improved production reliability by removing the control-flow and nil-response failure modes.
Stack Ruby, Fluentd, Ping AIC logs, JSON, CI/CD, Linux
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