CLO Data Assurance & QA Automation – Improving Data Accuracy & Reliability
Structured-Finance Analytics Intelligence Leader
(Trustee & Structured Finance Division)
Problem
Client' trustee operations processed 3,500+ deals/month across heterogeneous trustee data formats. The legacy ETL setup caused 20% manual QA overhead, inconsistent schema mapping, and frequent data reconciliation delays. With rising compliance requirements and offshore vendor dependencies, the bank needed an onshore, AI-driven data assurance platform to ensure accuracy, scalability, and audit-ready governance.
Approach
Built an AI-driven Data Transform & Assurance Platform that automated ingestion, validation, and mapping across hundreds of CLO trustee feeds. The solution replaced rigid ETL with a metadata-driven, self-healing engine that detects schema drift, standardizes data, and executes 500+ rule-based + ML validations. Integrated lineage dashboards, compliance tagging, and auto-correction loops to achieve transparency and end-to-end quality assurance—delivering faster refreshes, higher trust, and full audit traceability in U.S. Bank's CLO analytics.
Agents We Created
Specialized AI agents engineered to automate and optimize business operations
Schema Detection Agent
Data Validation Agent
QA & Reconciliation Agent
Lineage & Governance Agent
Anomaly Alert Agent
Tools Used
Cutting-edge technologies powering intelligent automation
Outcome Metrics
Measurable impact delivered through intelligent automation
Data accuracy across deals
Reduction in manual QA effort
Faster validation cycles vs legacy vendor model
Audit-ready lineage tracking for compliance
Schema drift disruptions with self-healing automation
Data trust and analytics confidence for U.S. Bank's CLO operations
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