AUTOMATION
Bank & Non-Bank Debt Reference Data Automation
Most blurs start upstream, in the security master. This turned a 45-minute manual set-up into a sub-1-minute automated one, without losing data quality or governance along the way.
Problem statement
Reference data feeds every hand-off in the chain, so a blur here travels everywhere downstream. Setting up and maintaining security reference data for both traditional (non-bank) debt and bank debt — syndicated and private — was a manual, largely person-dependent process. Every new instrument required someone to source data from multiple market vendors, reconcile conflicting fields, and key it into downstream systems by hand.
That manual step took roughly 45 minutes per instrument and sat directly upstream of loan administration, trustee services, and CLO servicing — so any error or delay propagated into accounting, investor reporting, and compliance. As deal volume grew, the process became both a bottleneck and a standing operational risk.
Approach
Five steps, applied in sequence — each one deliberately checked back against the big picture before moving to the next, so the detail work never drifts from the actual business problem.
Discover & diagnose
Mapped the full manual workflow end-to-end across loan administration, trustee, and CLO servicing teams to find exactly where time and risk concentrated — not just where it felt slow.
Model the domain
Defined a canonical reference-data model covering instrument identifiers, issuer/borrower hierarchies (anchored to LEI where available, the entity-identification standard the EDM Council helped establish industry-wide), pricing sources, and lifecycle events, so every downstream system would consume one consistent version of the truth.
Integrate the vendor layer
Evaluated, negotiated, and integrated new referential data sets from market data vendors — aligning formats and refresh cadence to the canonical model without breaking existing downstream consumers.
Build & automate
Architected and implemented the automated set-up and maintenance workflow, replacing manual entry with validated, rules-driven ingestion.
Govern & sustain
Layered in data quality and governance controls — validation rules, exception queues, clear ownership — so the automation stayed reliable as new instrument types were added.
What was built
A fully automated reference-data pipeline for both traditional and bank debt instruments. New instruments now flow from market data vendors through validated, rules-driven ingestion into the canonical model — with exceptions routed to a clear owner instead of silently breaking downstream reports.
The design deliberately kept the big picture in view at every step: the automation wasn't just built to run fast, it was built to fail safely, with the same governance rigor the manual process nominally had but rarely enforced in practice.
Business value
What it delivered
- Processing time cut from 45 minutes to under 1 minute per instrument — a roughly 98% reduction.
- Meaningful reduction in operational risk from manual keying errors.
- Freed operations staff from repetitive data entry to focus on exceptions and higher-value work.
- Improved downstream data quality across loan administration, trustee, and CLO servicing functions.