How improving data quality in delegated authority reporting unlocked new regulatory reporting capabilities and operational efficiency.
In the delegated authority space, the accuracy of MonthEnd Bordereaux reports—detailed spreadsheets of premiums and claims shared between Managing General Agents (MGAs) and carriers is paramount. Historically, this has been an administrative nightmare, with compliance, reserving, and financial reporting resting on the integrity of often poorly structured data received via spreadsheets and Excel. This case study explores how intelligent automation transformed this critical process.
The Bordereaux Challenge: Manual Pain and Compliance Risk
The bordereaux process is plagued by a "garbage in, garbage out" problem. Carriers receive hundreds of reports monthly, each presenting unique data quality challenges:
- Format and Mapping Inconsistencies: Every MGA uses a slightly different template, requiring extensive manual mapping and reconciliation before data can be ingested by core systems.
- Delayed Reconciliation: Errors are often only caught weeks or months after submission, leading to inaccurate reserving and regulatory submissions.
- High Operational Cost: Teams dedicate hundreds of hours monthly simply cleaning, correcting, and chasing missing information, diverting resources from analysis and strategy.
The Solution: AI as the Bordereaux Quality Engine
Implementing an Artificial Intelligence (AI) and Machine Learning (ML) solution allowed the carrier to stop treating bordereaux as simple files and start treating them as structured data streams. The AI platform was specifically trained to handle the variability inherent in delegated authority reporting, bridging the gap between external formats and internal data standards.
- Intelligent Data Ingestion: AI models were deployed to automatically "read" and interpret incoming MGA spreadsheets, regardless of template variations. The system identified column headers, extracted data types (even from merged cells), and normalized policy and claims information into a clean, unified data lake structure instantly.
- PreIngestion Validation (External & Internal): The AI platform cross-referenced key fields (e.g., policy numbers, client IDs, property addresses) against internal policy records and reliable third-party data sources. This automated verification drastically improved data accuracy at the point of entry, significantly reducing fraud potential and ensuring coverage limits were correctly tracked.
- Automated Root Cause Identification: The ML component moved beyond simple error flagging. It analyzed recurring errors across submissions, identifying the root cause—for instance, a specific MGA team repeatedly misclassifying premium types. This allowed the carrier to provide targeted feedback to partners, preventing future quality issues from occurring in the first place.
Outcome: Unlocking Trust and Regulatory Agility
The transformation delivered quantifiable operational and compliance benefits:
- 95% Reduction in Manual Processing Time: The monthly bordereaux reconciliation cycle dropped from 10 days to less than half a day.
- Instant Regulatory Readiness: With clean, verified data available in a structured format, regulatory reports were generated automatically and validated against internal policies instantly, providing the highest level of assurance to regulatory bodies.
- Enhanced Partner Relations: The AI tool generated transparent, actionable data quality reports for MGAs, fostering collaborative improvement rather than punitive audits.
By adopting AI to handle the heavy lifting of bordereaux data quality, the insurance carrier transformed a compliance burden into a competitive advantage, securing a trustworthy data foundation for reserving, reinsurance, and advanced risk modeling.